Coarse Spatial Resolution Research Articles

Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.

Mapping dinoflagellate blooms (Noctiluca and Alexandrium) in aquaculture production areas in the NW Iberian Peninsula with the Sentinel-2/3 satellites

The Galician Rías (northwestern Spain) are periodically affected by harmful algal blooms (HABs), mostly dinoflagellates, which pose a challenge to aquaculture activities due to the accumulation of biotoxins in shellfish. Typically, reddish blooms in the Rías are associated with non-toxic species like Noctiluca scintillans, with a few exceptions such as Alexandrium minutum, a producer of paralytic shellfish toxins (PST). Here, a useful approach is presented for monitoring reddish blooms through satellite imagery based on three case studies, two of them belonged to monospecific blooms of red Noctiluca scintillans, and the third to a bloom of Alexandrium spp. dominated by A. tamarense. In every case, a propulsive index was evaluated using Sentinel-2A/B satellites, which provide high spatial and spectral resolutions, combined with adequate atmospheric and sunglint correction by using the ACOLITE and C2RCC processors. This approach offers a simple and feasible method to accurately and timely map blooms of red N. scintillans and Alexandrium spp. in the study area, useful to detect the distribution of reddish blooms with synoptic observations for monitoring and aquaculture management purposes. Conversely, Sentinel-3A/B satellites with a relatively coarser spatial resolution, lacking adequate visualization and mapping of the extent of small blooms, did not accurately detect bloom footprints in the coastal bay region, although this sensor displays a set of suitable multispectral bands.

SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

Abstract. As a key component in the water and energy cycle, estimates of precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. However, current satellite-based precipitation products have a coarse spatial resolution (from 10 to 50 km) not meeting the needs of several applications (e.g., flash floods and landslides). The implementation of spatial downscaling methods can be a suitable approach to overcome this shortcoming. In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the integrated multisatellite retrievals for global precipitation measurement (IMERG) V06B daily precipitation product over a complex topographic and climatic area in southwestern Europe (Iberian Peninsula) in the period 2016–2018. By exploiting the soil-water balance equation, high-resolution surface soil moisture (SSM) and normalized difference vegetation index (NDVI) products were used as auxiliary variables. The spatial resolution of the IMERG daily precipitation product was downscaled from 10 to 1 km. An evaluation using 1027 rain gauge stations highlighted the good performance of the downscaled 1 km IMERG product compared to the original 10 km product, with a correlation coefficient of 0.61, root mean square error (RMSE) of 4.83 mm and a relative bias of 5 %. Meanwhile, the 1 km downscaled results can also capture the typical temporal and spatial variation behaviors of precipitation in the study area during dry and wet seasons. Overall, the SMPD method greatly improves the spatial details of the original 10 km IMERG product also with a slight enhancement of accuracy. It shows good potential to be applied for the development of high-quality and high-resolution precipitation products in any region of interest.

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m2) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.

Formaldehyde and hydroperoxide distribution around the Arabian Peninsula – evaluation of EMAC model results with ship-based measurements

Abstract. Formaldehyde (HCHO), hydrogen peroxide (H2O2) and organic hydroperoxides (ROOH) play a key role in atmospheric oxidation processes. They act as sources and sinks for HOx radicals (OH + HO2), with OH as the primary oxidant that governs the atmospheric self-cleaning capacity. Measurements of these species allow for evaluation of chemistry-transport models which need to account for multifarious source distributions, transport, complex photochemical reaction pathways and deposition processes of these species. HCHO is an intermediate during the oxidation of volatile organic compounds (VOCs) and is an indicator of photochemical activity and combustion-related emissions. In this study, we use in situ observations of HCHO, H2O2 and ROOH in the marine boundary layer (MBL) to evaluate results of the general circulation model EMAC (ECHAM5/MESSy2 Atmospheric Chemistry; European Center HAMburg, Modular Earth Submodel System). The dataset was obtained during the Air Quality and Climate Change in the Arabian Basin (AQABA) ship campaign around the Arabian Peninsula in summer 2017. This region is characterized by high levels of photochemical air pollution, humidity and solar irradiation, especially in the areas around the Suez Canal and the Arabian Gulf. High levels of air pollution with up to 12 ppbv HCHO, 2.3 ppbv ROOH and relatively low levels of H2O2 (≤0.5 ppbv) were detected over the Arabian Gulf. We find that EMAC failed to predict absolute mixing ratios of HCHO and ROOH during high-pollution events over the Arabian Gulf, while it reproduced HCHO on average within a factor of 2. Dry deposition velocities were determined for HCHO and H2O2 at night with 0.77±0.29 cm s−1 for HCHO and 1.03±0.52 cm s−1 for H2O2 over the Arabian Sea, which were matched by EMAC. The photochemical budget of H2O2 revealed elevated HOx radical concentrations in EMAC, which resulted in an overestimation of H2O2 by more than a factor of 5 for the AQABA dataset. The underestimated air pollution over the Arabian Gulf was related to EMAC's coarse spatial resolution and missing anthropogenic emissions in the model.

Evaluating the Drainage Density Characteristics on Climate and Drainage Area Using LiDAR Data

The purpose of this study is to identify the relationship between drainage density and climate, as represented by the climate aridity index, and to understand the relationship between drainage density and drainage area. A total of 121 study sites with low human impact, and a wide range of climate aridity index 0.3 (humid)–10.4 (arid), were selected based on the availability of light detection and ranging (LiDAR) data, producing a digital elevation model (DEM) with a spatial resolution of 1 m. A curvature-based method, incorporating both positive and negative curvature information, was used to extract the valley (drainage) network from the LiDAR-based DEMs. Drainage density and climate aridity index exhibited a monotonically increasing trend, contrary to the previous results that have shown a U-shaped relationship. This discrepancy was caused by the selection of watersheds with extensive human activity in the previous study. One-meter resolution DEM produced greater drainage density than the previous studies with a coarse spatial resolution of 30 m as small valleys are not detectable in low-resolution topography datasets. The discrepancy between the previous study and the current study results encouraged further investigation of the impact of the drainage area (watershed size). A negative correlation between drainage density and drainage area was reconfirmed, while a stronger decreasing trend was observed in arid regions than in humid regions.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation

The thermal infrared band of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra/Aqua satellite can provide daily, 1 km land surface temperature (LST) observations. However, due to the influence of cloud contamination, spatial gaps are common in the LST product, restricting its application greatly at the regional scale. In this article, to deal with the challenge of large gaps (especially complete data loss) in MODIS LST for local monitoring, a filling then spatio-temporal fusion (FSTF) method is proposed, which utilizes another type of product with all-sky coverage, but coarser spatial resolution (i.e., the 7 km China Land Data Assimilation System (CLDAS) LST product). Due to the great temporal heterogeneity of LST, temporally closer auxiliary MODIS LST images are considered to be preferable choices for spatio-temporal fusion of CLDAS and MODIS LST time-series. However, such data are always abandoned inappropriately in conventional spatio-temporal fusion if they contain gaps. Accordingly, pregap filling is performed in FSTF to make fuller use of the valid information in temporally close MODIS LST images with small gaps. Through evaluation in both the spatial and temporal domains for three regions in China, FSTF was found to be more accurate in reconstructing MODIS LST images than the original spatio-temporal fusion methods. FSTF, thus, has great potential for updating the current MODIS LST product at the global scale.

System design for a near real-time Caribbean Sargassum monitoring platform in the National Laboratory for Earth Observation (LANOT), Mexico. Implementing Spatial Data Science Tools.

A sudden change in global and regional environmental conditions has triggered the invasion of Sargassum algae in parts of the Caribbean coasts since 2014. To date, it has not been possible to revert the trend of seasonal Sargassum invasion, but some public institutions of subtropical countries are in the process of building monitoring systems based on satellite earth observation. Algorithms applied on high spatial resolution (but low revisiting frequency) data have been reported successful for Sargassum detection. GOES-16, MODIS (Aqua & Terra) and VIIRS imagery are acquired daily at the reception station of the National Laboratory for Earth Observation (LANOT), hosted in the Geography Institute, National Autonomous University of Mexico (UNAM). In this research, we implement a near real time Sargassum monitoring platform off the shore of Honduras, Belize and Mexico, based on the above-mentioned satellite imagery. The system design of this platform is first described, including the Big Data infrastructure for image acquisition and storage. Then, Sargassum potential presence is mapped from each of the three sensors, using Python-based processing tools and Sargassum detection algorithms. The coarse spatial resolution products obtained could complement higher spatial resolution studies by providing inputs for temporal modelling of propagation and onshore accumulation of Sargassum.

Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images by Encoder–Decoder Networks With Atrous Convolution

Passive microwave data is an important data source for the continuous monitoring of Arctic-wide sea ice concentration (SIC). However, its coarse spatial resolution leads to blurring effects at the ice-water divides, resulting in the great challenges of fine-scale and accurate SIC estimation, especially for regions with low SIC. Besides, the SIC derived by operational algorithms using high-frequency passive microwave observations has great uncertainties in open water or marginal ice zones due to atmospheric effects. In this article, a novel framework is proposed to achieve accurately SIC estimation with improved spatial details from original low-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) images, with joint the super-resolution (SR) and SIC estimation network. Based on the SR network, the spatial resolution of original AMSR2 images can be improved by four times, benefiting to construct AMSR2 SR features with more high-frequency information for SIC estimation. The SIC network with an encoder–decoder structure and atrous convolution, is employed to accurately perform the SIC retrieval by considering the characteristics of passive microwave images in the Arctic sea ice region. Experimental results show that the proposed SR-Aided SIC estimation approach can generate accurate SIC with more detailed sea ice textures and much sharper sea ice edges. With respect to MODIS SIC products distributed in Arctic scale, the proposed model achieves a root-mean-square error (RMSE) of 5.94% and mean absolute error (MAE) of 3.04%, whereas the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) SIC results have three and two times greater values of RMSE and MAE.

Using Nonstationary Depth-Frequency Curves to Characterize Local Precipitation Trends

Highlights Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.

Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China

Abstract. A long-term high-resolution national dataset of precipitation (P), soil moisture (SM), and snow water equivalent (SWE) is necessary for predicting floods and droughts and assessing the impacts of climate change on streamflow in China. Current long-term daily or sub-daily datasets of P, SM, and SWE are limited by a coarse spatial resolution or the lack of local correction. Although SM and SWE data derived from hydrological simulations at a national scale have fine spatial resolutions and take advantage of local forcing data, hydrological models are not directly calibrated with SM and SWE data. In this study, we produced a daily 0.1∘ dataset of P, SM, and SWE in 1981–2017 across China, using global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. Global 0.1∘ and local 0.25∘P data in 1981–2017 are merged to reconstruct the historical P of the 0.1∘ China Merged Precipitation Analysis (CMPA) available in 2008–2017 using a stacking machine learning model. The reconstructed P data are used to drive the HBV hydrological model to simulate SM and SWE data in 1981–2017. The SM simulation is calibrated by Soil Moisture Active Passive Level 4 (SMAP-L4) data. The SWE simulation is calibrated by the national satellite-based snow depth dataset in China (Che and Dai, 2015) and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. Cross-validated by the spatial and temporal splitting of the CMPA data, the median Kling–Gupta efficiency (KGE) of the reconstructed P is 0.68 for all grids at a daily scale. The median KGE of SM in calibration is 0.61 for all grids at a daily scale. For grids in two snow-rich regions, the median KGEs of SWE in calibration are 0.55 and −2.41 in the Songhua and Liaohe basins and the northwest continental basin respectively at a daily scale. Generally, the reconstruction dataset performs better in southern and eastern China than in northern and western China for P and SM and performs better in northeast China than in other regions for SWE. As the first long-term 0.1∘ daily dataset of P, SM, and SWE that combines information from local observations and satellite-based data benchmarks, this reconstruction product is valuable for future national investigations of hydrological processes.

Remote Sensing for Water Resources and Environmental Management

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on “Remote sensing for water resources and environmental management” was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales.

Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100)

AbstractFuture global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632.

Hydrological and Hydraulic Modeling Applied to Flash Flood Events in a Small Urban Stream

In flood area mapping studies, hydrological-hydraulic modeling has been successfully applied around the world. However, the object of study of most of the research developed in Brazil is medium to large channels that use topographical and hydrometeorological data of coarse spatial and temporal resolution. Thus, the aim of this study is to investigate coupled modeling in a small urban channel, using high-resolution data, in the simulation of flood events in a small urban channel, located in Campo Grande, Mato Grosso do Sul. In this study, we used the HEC-HMS and HEC-RAS programs, where topographic information, land use, land cover, and observed data from rain gauges, water level, and flow sensors from 2015 to 2018 were used as input data. To calibrate and validate the hydrological model, four events were used that occurred during the monitoring period, while in the hydraulic model we chose a historical event that caused great disturbances. We then generated flood scenarios with representative synthetic rainfall for a basin, with return times of 5, 10, 50, and 100 years. We observed a good fit in the calibration and validation of the HEC-HMS, with values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92. In HEC-RAS, we obtained values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92 for the calibration, and in the validation, real images of the event prove the computed flood spot sources. We observed that rain with a return time of less than five years provides areas of flooding in several regions of the channel, and in critical channeled sections, the elevation and speed of the flow reach 5 m and 3 m·s−1, respectively. Our results indicate that the channel already has a natural tendency towards flooding in certain stretches, which become more compromised due to land use and coverage and local conditions. We conclude that the high-resolution coupled modeling generated information that represents local conditions as well, showing how potential failures of drainage in extreme scenarios are possible, thus enabling the planning of adaptations and protection measures against floods.

Accounting for survey design in Bayesian disaggregation of survey-based areal estimates of proportions: An application to the American Community Survey

Understanding the effects of social determinants of health on health outcomes requires data on characteristics of the neighborhoods in which subjects live. However, estimates of these characteristics are often aggregated over space and time in a fashion that diminishes their utility. Take, for example, estimates from the American Community Survey (ACS), a multiyear nationwide survey administered by the U.S. Census Bureau: estimates for small municipal areas are aggregated over 5-year periods, whereas 1-year estimates are only available for municipal areas with populations >65,000. Researchers may wish to use ACS estimates in studies of population health to characterize neighborhood-level exposures. However, 5-year estimates may not properly characterize temporal changes or align temporally with other data in the study, while the coarse spatial resolution of the 1-year estimates diminishes their utility in characterizing neighborhood exposure. To circumvent this issue, in this paper we propose a modeling framework to disaggregate estimates of proportions derived from sampling surveys, which explicitly accounts for the survey design effect. We illustrate the utility of our model by applying it to the ACS data, generating estimates of poverty for the state of Michigan at fine spatiotemporal resolution.

Spatial downscaling of satellite soil moisture products based on apparent thermal inertia: Considering the effect of vegetation condition

Many applications such as irrigation management and flood predictions require soil moisture (SM) estimates at fine-resolutions. Soil moisture estimates of current satellite missions are normally at coarse spatial resolutions, such as 25–36 km for Soil Moisture and Ocean Salinity (SMOS) SM data. Many spatial downscaling approaches have been developed to get finer resolution SM. Apparent thermal inertia (ATI), as a reliable approximation of thermal inertia (TI) that could be derived from daily fine-resolution optical/thermal remote sensing (RS) data, has been testified to be highly related to SM. However, the empirical relationship between ATI and SM varies with vegetation cover density. The main purpose of this study is to develop an ATI-based SM downscaling method that is free of vegetation cover effects. The study was conducted in Naqu areas in the central Tibetan Plateau (TP) in southwest China, where spatially intensive in-situ SM measurements were routinely obtained. European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture combined product (version 4.2) was used for this study. The fine-resolution (1 km) ATI data were derived from the Collection 6 MODerate resolution Imaging Spectroradiometer (MODIS) products. A modified quantile-quantile (Q-Q) adjustment method was applied to correct the bias of the CCI SM data before downscaling. A non-linear regression model between bias-corrected CCI SM and fine resolution ATI was built to disaggregate the bias-corrected SM to spatial resolution of 1 km. In this model, Enhanced Vegetation Index (EVI) was introduced to globally and quantitatively adjust the vegetation effect in the relationship between SM and ATI. The bias-corrected CCI SM and the downscaled 1 km resolution SM were evaluated using the in-situ SM measurements obtained from the Multi-scale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) in the study area. The results indicate that the proposed modified Q-Q adjustment method effectively reduced the bias of the original CCI SM data (bias = 0 m3/m3). The downscaled 1 km resolution SM shows fine scale spatial variability of soil moisture and preserves the accuracy (R = 0.552) and temporal variability of the bias-corrected CCI SM. The accuracies of the downscaled SM over areas with different vegetation cover show quite good consistency. The uncertainties of the proposed SM spatial downscaling method were comprehensively analyzed. The proposed ATI-based SM downscaling algorithm is applicable to generating representative and consistent high spatial resolution SM over areas with different vegetation cover density.

A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes

Accurate estimation of mountain vegetation gross primary productivity (GPP) at fine spatial resolutions offers opportunities to better understand mountain ecosystems’ feedback to the global climate system. Eco-hydrological models have great advantages in simulating mountain vegetation photosynthesis, but their large-scale applications remain challenging at fine spatial resolutions due to the computing resources. In this work, a scheme by integrating an eco-hydrological model called Boreal Ecosystem Productivity Simulator-TerrainLab (BTL) with the linear and non-linear downscaling processes, was developed to obtain large-scale mountain vegetation GPP at the 30 m resolution over four watersheds. Firstly, two coarse spatial resolution GPP were simulated by BTL at 480 m and 120 m. Then, the 30 m resolution GPP was estimated by a linear downscaling process modelled at 120 m and a non-linear downscaling process modelled from 480 m to 120 m. The 30 m resolution BTL-simulated GPP was served as reference for evaluation. Results showed that the Root-Mean-Square-Error (RMSE) after downscaling was decreased by 110 gCm-2year−1 compared to the 120 m resolution BTL-simulated GPP (500 gCm−2 year−1) at the 30 m resolution, highlighting the effectiveness of the proposed scheme in recovering the topographic variations of mountain vegetation GPP at fine spatial resolutions. Compared to the 120 m resolution BTL-simulated GPP (351 gCm−2 year−1), RMSE after downscaling was decreased by 156 gCm−2 year−1 at the 120 m resolution, indicating that the proposed scheme is feasible in correcting GPP errors at coarse spatial resolutions. More specifically, the non-linear downscaling process was observed to effectively improve GPP estimates after linear downscaling, suggesting that the spatial scaling errors in coarse estimates should be considered in the downscaling process. Our study indicates that the scheme that runs eco-hydrological models at coarse resolutions and then downscales them by surface heterogeneity is a practical approach for obtaining large-scale mountain vegetation GPP at fine spatial resolutions.