Comment on egusphere-2023-845

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

Regional flow duration curve estimation in small ungauged catchments using instantaneous flow measurements and a censored data approach

The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors. Global space-borne sensors are not good enough to acquire an image at fine spatial and high temporal (FSHT) resolutions simultaneously. In this article, we propose a computationally efficient technique to create a FSHT resolution image using a ground-based data processing system. Resourcesat-2, part of the Indian Space Research Organization’s (ISRO’s) mission, carries linear imaging self-scanners (LISS III and LISS IV) and an advanced wide-field sensor (AWiFS) on board. The spatial and temporal resolutions of LISS III are 23.5 m and 24 days and those of AWiFS are 56 m and 5 days, respectively. The proposed method creates a synthetic FSHT resolution image with 23.5 m spatial and 5 day temporal resolution. This method is referred to as ‘LISS III spatial and AWiFS temporal’ (LSAT) data fusion. The LSAT data fusion method is based on a sub-pixel relationship between the images of a single AWiFS–LISS III image pair, which was acquired before or after the ‘prediction date’: a synthetic LISS III image for the time is ‘predicted’ (synthesized) using an AWiFS image at time and a single AWiFS–LISS III image pair at time , where . The LSAT model was tested on simulated and real data sets acquired by the LISS III and AWiFS sensors. The proposed method was compared with the recently developed spatiotemporal data fusion methods. The experimental results demonstrate that the method is computationally efficient and shows consistent prediction accuracy in retrieving surface reflectance changes.

Exploring the Role of Domain Partitioning on Efficiency of Parallel Distributed Hydrologic Model Simulations Spatially distributed hydrologic models of watersheds and river basins are data and computation intensive because of the combined nature of hydrodynamics, complex forcings and heterogeneous parameter fields. Application of these models at fine temporal and spatial resolutions, and on large problem domains, is facilitated by parallel computation on multi-processor clusters. Notably, the computation efficiency of parallel simulations is crucially determined by the efficiency with which data are divided-and-distributed in a multiprocessor environment and how the information is shared between processors. While numerous data partitioning algorithms exist and have been extensively studied in computer science literature, detailed elucidation of the role of hydrologic model structure on data partitioning has not been presented yet. In addition, the relative role of computational load balance and interprocessor communication on parallel computation efficiency of a hydrologic model is not known. Considering the unstructured domain discretization scheme used in PIHM hydrologic model as an example, the paper first presents a generic methodology for incorporating hydrologic factors in optimal domain partitioning algorithms. The partitions are then used to explore the isolated role of computation load balance and interprocessor communication on parallel efficiency. Results confirm that parallel simulations on partitions that minimize interprocessor communication and divide the computational load equally are the most efficient. More importantly, load balance between processors is observed to be a more sensitive control on parallel efficiency than minimization of interprocessor communication. Further analyses of the efficiency and scalability of the parallel code for different partitioning configurations reveal a direct correspondence between parallel efficiency and theoretical metrics such as load balance ratio and communication to computation ratio. Results indicate that theoretical metrics can be used for the selection of best partitions before computationally intensive parallel simulations are performed. The study serves as a proof-of-concept evaluation of the impact of computation and communication on the efficiency of parallelized distributed hydrologic models at multiple resolutions.

Lagrangian detection and tracking algorithms are frequently used to study the development, distribution, and trends of extratropical cyclones. Past research shows that results from these algorithms are sensitive to both spatial and temporal resolution of the gridded input fields, with coarser resolutions typically underestimating cyclone frequency by failing to capture weak, small, and short-lived systems. The fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) offers finer resolution, and therefore more precise information regarding storm locations and development than previous global reanalyses. However, our sensitivity tests show that using ERA5 sea-level pressure fields at their finest possible resolution does not necessarily lead to better cyclone detection and tracking. If a common number of nearest neighbors is used when detecting minima in sea-level pressure (like past studies), finer spatial resolution leads to noisier fields that unrealistically break up multi-center cyclones. Using a common search distance instead (with more neighbors at finer resolution) resolves the issue without smoothing inputs. Doing this also makes cyclone frequency, lifespan, and average depth insensitive to refining spatial resolution beyond 100 km. Results using 6-h and 3-h temporal resolutions have only minor differences, but using 1-h temporal resolution with a maximum allowed propagation speed of 150 km h-1 leads to unrealistic track splitting. This can be counteracted by increasing the maximum propagation speed, but modest sensitivity to temporal resolution persists for several cyclone characteristics. Therefore, we recommend caution if applying existing algorithms to temporal resolutions finer than 3-h and careful evaluation of algorithm settings.

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.

<p>The hydrography of the prairie basins is complicated by the existence of numerous land depressions, known as prairie potholes, which can retain a substantial amount of surface runoff. Consequently, the runoff production in the prairies follows a fill, spill, and merging mechanism, which results in a dynamic contributing area that makes the streamflow simulation challenging. Existing approaches to represent the potholes’ dynamics, in different hydrological models, use either a lumped or a series of reservoirs that contribute flow after exceeding a certain storage threshold. These approaches are simplified and do not represent the actual dynamics of the potholes nor their spatial water extents. Consequently, these approaches may not be useful in capturing the potholes’ complexities and may not be able to accurately simulate the complex prairie streamflow. This study advances towards more accurate and physically-based streamflow simulation in the prairies by implanting a physically-based runoff generation algorithm (Prairie Region Inundation MApping, PRIMA model) within the MESH land surface model, and is referred to as MESH-PRIMA. PRIMA is a recently developed hydrological routing model that can simulate the lateral movement of water over prairie landscape using topographic data provided via DEMs. In MESH-PRIMA, MESH handles the vertical water balance calculations, whereas PRIMA routes the water and determines the amount of water storage and surface runoff. The streamflow simulations of MESH-PRIMA (using different DEM resolution as a topographic input) and MESH with its existing conceptual pothole dynamics algorithm are tested on a number of pothole-dominated watersheds within Saskatchewan, Canada, and compared against observed flows. MESH-PRIMA provides improved streamflow and peak flow simulation, compared to that of MESH with its conceptual pothole algorithm, based on the metrics evaluated for the simulations. MESH-PRIMA shows potential for simulating the actual pothole water extents when compared against water areas obtained from remote sensing data. The use of different DEM resolution changes the resulting pothole water extent, especially for the small potholes as they are not detected in the coarse DEM. MESH-PRIMA can be considered as a hydraulic-hydrologic model that can be used for better understanding and accurate representation of the complex prairie hydrology.</p>

<p>India is one of the world's most flood-prone countries, with 113 million people exposed to floods. Large-scale hydrological models integrated with complicated Navier–Stokes based hydraulic, and inundation models traditionally address flood preparedness, control, and mitigation. In addition to being highly data-intensive at the fine spatial and temporal resolution, this approach has a considerable computational cost that limits real-time applications. We employ the parameter-free Dynamic Budyko (DB) hydrological model to map observed precipitation with gridded runoff to overcome data scarcity. We propose a time-efficient Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCI-FRIM) framework that can be used with any hydrological model to generate a probability map of inundation. To model the catastrophic flood extents that the state of Kerala in India experienced during August 2018, we use gridded 0.25 deg × 0.25 deg IMD precipitation data. We use a parameter-free iterative approach to update flood velocity by assuming that river velocity does not fluctuate geographically across a particular river network at a given time instant. We pre-compute the iterative velocity and model the relationship between flood velocity-discharge and discharge-inundation height for each reach by combining the globally available SRTM/ASTER DEMs with empirically obtained river-reach geometry data (JPL). We compute the reach slope from the absolute vertical error-prone DEM by segmenting the river network into a series of independent channels and extracting the relationship between the channel pixel's elevation and the pixel's distance to the pour point. We use the Height Above Nearest Drainage (HAND) to map the probabilistic spatial extent corresponding to an ensemble of derived reach inundation heights. We then compare the proposed model with observed flood data points provided by the Kerala State Disaster Management Authority (KSDMA). The model captures up to 52% of 370,000 flood data points in a single run for the peak flood day within 15 minutes on a desktop computer. With reliable estimates of empirical bankfull discharge, the proposed model can achieve higher accuracy in lesser time.</p>

The role of irrigation and artificial drainage in the hydrologic cycle and the transport of solutes in a small agricultural catchment in central Washington’s Yakima Valley were explored using hydrologic, chemical, isotopic, agedating, and mineralogical data from several environmental compartments, including stream water, ground water, overland flow, and streambed pore water. A conceptual understanding of catchment hydrology and solute transport was developed and an inverse end-member mixing analysis was used to further explore the effects of agriculture in this small catchment. The median concentrations of major solutes and nitrates were similar for the single field site and for the catchment outflow site, indicating that the net effects of transport processes for these constituents were similar at both scales. However, concentrations of nutrients were different at the two sites, suggesting that field-scale variations in agricultural practices as well as nearstream and instream biochemical processes are important components of agricultural chemical transformation and transport in this catchment. This work indicates that irrigation coupled with artificial drainage networks may exacerbate the ecological effects of agricultural runoff by increasing direct connectivity between fields and streams and minimizing potentially mitigating effects (denitrification and dilution, for example) of longer subsurface pathways. 2 Effect of Agricultural Practices on Hydrology and Water Chemistry, Yakima River Basin, Washington In the work presented here, the hydrologic cycle and the transport of solutes in a small, irrigated catchment in the Yakima Valley of central Washington are described on the basis of the results of analyses of various hydrologic, chemical, isotopic, age dating, and mineralogical data from several environmental compartments, including stream water, ground water, overland flow, and streambed pore water. A conceptual model of catchment hydrology and solute transport is developed and an inverse end-member mixing analysis is used to further explore the effects of agriculture in this small irrigated catchment. These data were collected as part of a larger effort by the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) program. To gain insights into how environmental processes and agricultural practices interact to determine the transport and fate of agricultural chemicals in the environment, the USGS NAWQA program conducted in-depth investigations at five agricultural study areas across the country during 2003–04. The design of the overall study is described in detail by Capel and others (2008). Companion studies report on the transport and processing of agricultural chemicals in surface water (Domagalski and others, 2008; Duff and others, 2008); transport in the subsurface (Green and others, 2008; Steele and others, 2008), and ground-water/ surface-water interactions (Duff and others, 2008; Essaid and others, 2008; Puckett and others, 2008). Description of Study Area The DR2 catchment is a small (5.5 km2) subbasin in the Yakima Valley of central Washington (fig. 1). A detailed description of the catchment is available in Payne and others (2007). Three characteristics of the catchment particularly relevant to the transport and fate of agricultural chemicals are: (1) irrigation practices, (2) crop heterogeneity, and (3) subsurface drainage. These characteristics are discussed here briefly. Land use in the DR2 catchment is nearly 90 percent agriculture, and, with less than 18 cm of annual precipitation, 95 percent of the agriculture is irrigated. The demand for irrigation water is met by withdrawals from the Yakima River that are delivered to the catchment through the Sunnyside Canal (fig. 1) and dispersed throughout the catchment by secondary delivery canals. At the individual field scale, water is applied to crops by various methods, including rill, drip, and sprinkler systems. Although the use of rill irrigation is slowly diminishing in the catchment as growers convert to more efficient sprinkler and drip systems, it remains the predominant method due to economic, planting, harvesting, and other crop management considerations that make more efficient systems less practical. A wide variety of crops are grown in the DR2 catchment (Payne and others, 2007). During the study period, crops and other agricultural activities included corn, grapes (for both wine and juice), asparagus, alfalfa, forage grass, pasture, and dairy/feedlot operations. Not only do crops—and therefore irrigation practices and chemical applications—vary considerably across this small catchment, they also vary from year to year in many fields. Shallow subsurface flow in the area has been modified by an extensive system of buried drains. These drains exert a strong influence on the shallow ground-water system and its connection to surface water. However, most of the drainage system has been in place for many decades, and apart from the large, regional drains, the location and extent of the drains are not well known.

Hydrological models include errors when reproducing real-world observations, due to uncertainties in their components that inevitably propagate to the simulated variable. A large body of research in streamflow prediction blends statistical learning into the hydrological sciences, modelling river discharge using meteorological variables and catchment attributes as predictors of observed streamflow.We developed a novel hybrid framework that integrates information from the process-based global hydrological model PCR-GLOBWB to reduce prediction errors in streamflow simulations. Our statistical methodology employs simulated streamflow and state variables from PCR-GLOBWB as additional predictors of observed river discharge. These model outputs provide supplemental information that is effectively used in a random forest, trained on a global database of streamflow measurements, to improve estimates of simulated river discharge across the globe. PCR-GLOBWB was run for the years 1979-2019 at 30arcmin and daily resolution, and the simulated state variables were then aggregated to monthly time steps. A single random forest model was trained with these state variables, meteorological data and catchment attributes, as predictors of observed streamflow from 2286 stations worldwide.Results based on cross-validation show that the model is capable of discerning between a variety of hydro-climatic conditions and river flow dynamics, improving KGE of PCR-GLOBWB simulations at more than 80% of testing locations and increasing median KGE from -0.02 in uncalibrated runs to 0.52 after post-processing. Performance boosts are usually independent of availability of streamflow data at a particular station, thus making our method a potential candidate in addressing prediction in poorly gauged and ungauged basins.Further research is still needed to test the potential influence of additional predictors describing catchment and time-series behaviour. Cluster analysis is required to understand why the post-processing framework still performs poorly at some stations. For prediction purposes, future efforts should also be directed at testing the model at higher spatial resolutions globally, and at finer temporal resolutions.

Human activities have dramatically altered the distribution and abundance of species, and our impacts are likely to increase in the near future. Conservation efforts are typically faced with scarce resources, forcing us to prioritize areas based in part on estimates of their conservation value. Two major factors in conservation value are a species uniqueness and its extinction risk. Though these ideas are multidimensional, one important component of uniqueness is evolutionary distinctness, while risk is strongly related to geographic range size. These components are combined in an assemblage-level measure called phylogenetic endemism (PE), which measures the degree to which the species in an assemblage are small-ranged and phylogenetically distinct. Broad-scale patterns and correlates of PE are becoming better known for a variety of groups, and have been shown to depend on current climate, geographic isolation and long-term climate stability. Human impacts (e.g., land cover changes), are likely to shape PE as well, though the coarse resolution of most previous studies may make this difficult to detect. Overall, PE patterns at fine spatial and temporal resolutions are not well understood. Here, we fill this gap using data from the North American Breeding Bird Survey. These data comprise a long-term annual record with fine spatial resolution and a near-continental extent. We assess geographic patterns and trends in PE, and relate these to a range of putative predictor variables including measures of current climate, land cover, long-term and recent climate change. Bird PE is concentrated in three main hotspots: the west coast, the southeast and south-central Canada east of the Rockies. High PE values tended to occur in regions with high temperatures and stability in temperature, both in the long (21,000 year) and short (35 year) time scales. PE patterns are driven more strongly by patterns of range size than phylogenetic distinctiveness, and are trending gradually upward, driven by increasingly frequent sightings of small-ranged species. These results indicate the importance of climate stability on multiple time scales in influencing endemism patterns and suggest a surprisingly minor influence of direct human land use. The increase in PE through time may reflect successful conservation efforts that have led to population recoveries of some small-ranged species.

Two-component isotopic hydrograph separation (IHS) was developed to determine the event- and pre-event components of a single storm event. Its application for several sucessive events requires repeated determination of isotopic signatures of end-members (precipitation, pre-event component) for each event. The existence of several possible alternative signatures results in differences in calculated contributions of event-/pre- event components. This article addresses the question of how big the differences could be in small mountain catchments with different methods for detemining the end member signatures. We analyzed data on isotopic composition of daily/event precipitation at different elevations in two catchments located in the highest part of the Carpathians in July 2014.The isotopic composition of streamflow sampled every 4-6 hours was analyzed as well. Elevational gradients of δ18O and δ2H in precipitation in the study period were -0.18 ‰ 100 m-1 and -1.1 ‰ 100 m-1, respectively. An elevation gradient in deuterium excess (0.29 ‰ 100 m-1) was also found. Precipitation on the windward side of the mountains was isotopically lighter than expected for a given rain gauge elevation. Five large rainfall-runoff events occurred in the study period in the meso-scale catchment of the Jalovecký creek (Western Tatra Mountains, area 22.2 km2) and in the headwater catchment of the Škaredý creek (High Tatra Mountains, area 1.4 km2). Isotopic hydrograph separation was conducted using eight options for the isotopic signatures of event and pre-event water. The isotopic signature of the event water (rainfall) was alternatively represented by data from high or low elevations. Pre-event water was represented either by the streamflow before the event or by the value taken from the statistics of the long-term data on isotopic composition of the stream. Both isotopes (18O and 2H) were used to calculate event water fractions during peak flows of individual events. Calculated peak flow event water fractions were below 0.2-0.3 for most events. However, the differences in calculated event water fractions for alternative isotopic composition of end-members were significant even if we did not take into account changes in isotopic composition during individual rainfalls. Coefficients of variation for event water fractions calculated for various options varied during individual events from 0.14 to 0.36. It is therefore perhaps better to use a range of possible values instead of a single accurate number to interpret the IHS results. Hydrograph separations based on 18O and 2H provided similar results.

Abstract. Formaldehyde (HCHO) is either directly emitted from sources or produced during the oxidation of volatile organic compounds (VOCs) in the troposphere. It is possible to infer atmospheric HCHO concentrations using space-based observations, which may be useful for studying emissions and tropospheric chemistry at urban to global scales depending on the quality of the retrievals. In the near future, an unprecedented volume of satellite-based HCHO measurement data will be available from both geostationary and polar-orbiting platforms. Therefore, it is essential to develop retrieval methods appropriate for the next-generation satellites that measure at higher spatial and temporal resolution than the current ones. In this study, we examine the importance of fine spatial and temporal resolution a priori profile information on the retrieval by conducting approximately 45 000 radiative transfer (RT) model calculations in the Los Angeles Basin (LA Basin) megacity. Our analyses suggest that an air mass factor (AMF, a factor converting observed slant columns to vertical columns) based on fine spatial and temporal resolution a priori profiles can better capture the spatial distributions of the enhanced HCHO plumes in an urban area than the nearly constant AMFs used for current operational products by increasing the columns by ∼ 50 % in the domain average and up to 100 % at a finer scale. For this urban area, the AMF values are inversely proportional to the magnitude of the HCHO mixing ratios in the boundary layer. Using our optimized model HCHO results in the Los Angeles Basin that mimic the HCHO retrievals from future geostationary satellites, we illustrate the effectiveness of HCHO data from geostationary measurements for understanding and predicting tropospheric ozone and its precursors.

In Canada, the majority of urban energy demand services the transportation or building sectors, primarily with non-renewable energy sources including gasoline and natural gas. As a result, these two sectors account for 70% of urban greenhouse gas (GHG) emissions. The objective of this paper is to explore the potential for co-benefits when simultaneously electrifying transportation and building demand sectors while expanding variable renewable energy (VRE) production. The investigation uses a novel integrated framework of the transportation, building, and electricity sectors to represent the operational implications of demand side flexibility on both the demand and supply side of the energy system. This original approach allows for very fine temporal and spatial resolution within models, while still performing a multi-sector analysis. First, the activity-based transportation model produces passenger travel demand profiles, allowing for investigation of potential electricity demand and demand response from electric vehicles with high spatial and temporal resolution. Second, the archetype-based building model predicts electricity demand of the residential building sector, allowing for investigation into demand-side management strategies such as load-shifting, building retrofits, and changes in appliance technology. Third, the electricity system production cost dispatch model is used to model the operations of Regina's electricity grid and has a spatial resolution capable of assessing individual and connected positive energy districts as well as VRE integration. Through linking of these three models, the effects of consumer flexibility in transportation and building energy demand are explored, especially in the context of introducing much needed flexibility for large-scale VRE integration. A utility-controlled demand response (DR) strategy is explored as means for Regina to reach their renewable target, along with battery storage. Various pathways to Regina's target are considered, based on the various proposed scopes of the target. The results show that Regina can meet their renewable target with large-scale rooftop solar and wind capacity. DR strategies are marginally effective in aiding toward the renewable target, but, when implemented in conjunction with battery storage, is able to get Regina to within 1% of their renewable target.

Seasonal hydrological forecasts are of critical importance for the effective management of water resources, particularly in complex and vulnerable basins such as the Mediterranean area which have both a deficitary water regimen and a high anthropogenic pressure. Nevertheless, inaccuracies in meteorological inputs can propagate through hydrological models, amplifying uncertainties in flow predictions. It is imperative to rectify these forecasts, whether meteorological or hydrological, to enhance prediction reliability and provide robust data for informed decision-making. This study proposes an advanced framework for seasonal hydrological forecasting that integrates raw and corrected weather forecasts with distributed hydrological modeling and sophisticated post-processing techniques to enhance flow prediction accuracy in the Júcar River in Spain as a representative case study of the Mediterranean area.The catchment hydrological model was implemented using the model TETIS v9.1 wich was calibrated and validated using observed records from 1981 to 2019 at seven control points. To do this, a split sample test was conducted using the period 2009–2019 for calibration, and the rest of the time series for validation (1981–2008). The process involved refining parameter maps to ensure good or acceptable performance at all control points. Meteorological data were sourced from the W5E5 dataset, downscaled to a 0.09° resolution using ERA5-Land. This improved the spatial and temporal resolution of the hydrological model. Once we have established an acceptable hydrological model,, the seasonal forecast hindcasts were evaluated using meteorological inputs from global forecasting systems, including ECMWF-SEAS5, CMCC_SPSv35, DWD_GCFS21, and MeteoFrance System8. To address uncertainties in the meteorological forecasts and their propagation to hydrological outputs, two complementary correction strategies were implemented. First, artificial intelligence(fuzzy logic) was applied to correct meteorological inputs before integration into the hydrological model, assuming errors originate solely from meteorological data and treating the hydrological model as a “perfect” simulator. Second, a hydrological error model was developed to identify and adjust discrepancies between simulated and observed flows, addressing systematic biases and errors in the hydrological simulation.The results demonstrated that forecasts based on corrected meteorological inputs exhibited significant accuracy improvements compared to those using unprocessed inputs. The hydrological error model further enhanced prediction reliability by mitigating systematic biases. These findings underscore the effectiveness of combining meteorological forecasts with AI-driven corrections to address uncertainties, thereby improving the robustness of seasonal hydrological predictions. This study highlights the potential for integrating advanced correction techniques into seasonal hydrological forecasting frameworks, offering a replicable methodology for other basins with similar complexities. The proposed framework enhances both the reliability and applicability of forecasts, ensuring their relevance for effective decision-making in complex hydrological systems as the Mediterranean area. The improved accuracy of these forecasts provides a sound scientific support for adaptive water resource management, particularly in the face of increasing climatic variability and environmental changes.Acknowledgments: This study was funded by the Colombian Ministry of Science, Technology, and Innovation (MINCIENCIAS) through the Call for Doctorates Abroad 885-2; by the Valencian Regional Government through the WATER4CAST 2.0 (CIPROM/2023/5) research project; and Spanish Ministry of Science and Innovation through the research project TETISPREDICT (PID2022-141631OB-I00).