Resolution Of Input Data Research Articles

Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95% faster than the Central Processing Unit (CPU) benchmark algorithm using 33% less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.

Effects of input data accuracy, catchment threshold areas and calibration algorithms on model uncertainty reduction

AbstractLow resolution of input data and equifinality in model calibration can lead to inaccuracy and insufficient reflection of spatial differences, thereby increasing model errors. However, the impact of input data accuracy, catchment threshold area, and calibration algorithm on model uncertainty reduction has not yet been well understood. The sequential uncertainty fitting version 2 (SUFI‐2) that is linked with the Soil and Water Assessment Tool (SWAT) in the package called SWAT Calibration Uncertainty Programs (SWAT‐CUP) was introduced to quantify the effects of different input data resolutions on parameter sensitivity and model uncertainty in the Jinghe River watershed, and the effects of different sub‐basin delineations and other two calibration algorithms on model uncertainty were also comparatively analysed. (i) USLE_C, EPCO, ALPHA_BNK, and CN2 are the most sensitive parameters among all SWAT projects. When the change of digital elevation model (DEM) resolution is small, the sensitivity of parameters does not change obviously. When the DEM resolution changes significantly, BIOMIX, LAT_SED, USLE_K, and CH_N1 become highly sensitive parameters by replacing OV_N, SMTMP, SURLAG, and USLE_P. However, the change in land use resolution has little impact on parameter sensitivity, with only a slight change in the sensitivity ranking of specific parameters. (ii) Model uncertainty responded to changes in the resolution of DEM more than land use. Most of the runoff simulations had smaller uncertainties (P factor, R factor, percentage of bias [PBIAS]) than sediment. High resolution DEM data reduced model uncertainty, but the models with 2000 m DEM resolution also achieved small uncertainty. Small catchment threshold area leads to high uncertainty of the model, and large catchment threshold areas decrease the model uncertainty. The model has relatively good simulation effects in runoff and sediment when the catchment threshold area was 2000 km2. (iii) The SWAT model has different simulation deviations and uncertainties in different calibration algorithms, the SUFI‐2 and generalized likelihood uncertainty estimation (GLUE) algorithms show better applicability than particle swarm optimization (PSO). The NSE indicators of the three algorithms are in the following order: SUFI‐2 &gt; GLUE &gt; PSO for runoff, and GLUE &gt; SUFI‐2 &gt; PSO for sediment. This study helps us understand the cause, knowledge of which moves from the particular to the general by the comprehension of essence, power, and nature in reducing model uncertainty.

Testing sensitivity of hydrological operational models to finer resolution of input data for purpose of applying high resolution Destination Earth forecasts. Case study, part one

Testing sensitivity of hydrological operational models to finer resolution of input data for purpose of applying high resolution Destination Earth forecasts. Case study, part one

Comparison of methods to aggregate climate data to predict crop yield: an application to soybean

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration

Abstract The isotopic composition of evapotranspiration δET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculate δET, though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested the δET uncertainty using the mean point [σET (mean)] and median point [σET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. The σET (mean) would be greater than σET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter of r2 &gt; 0.8, around 70% of σET (mean) was greater than σET (median). This phenomenon might be due to the normality of the vapor concentration Cυ producing the asymmetric distribution of 1/Cυ. The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentration Cυ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions

In recent years, there has been a growing interest in flood susceptibility modeling. In this study, we conducted a bibliometric analysis followed by a meta-data analysis to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in flood susceptibility modeling. Relevant publications were retrieved from the Web of Science database to identify the leading authors, influential journals, and trending articles. The results of the meta-data analysis indicated that hybrid models were the most frequently used prediction models. Results of bibliometric analysis show that GIS, machine learning, statistical models, and the analytical hierarchy process were the central focuses of this research area. The analysis also revealed that slope, elevation, and distance from the river are the most commonly used factors in flood susceptibility modeling. The present study discussed the importance of the resolution of input data, the size and representation of the training sample, other lessons learned, and future research directions in this field.

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 ​m ​× ​16 ​m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 ​m3⋅ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.

Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring

Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.

Phase transitions between tropical, subtropical, and extratropical cyclones: A review from IWTC-10

This review, which was adapted from a Tenth International Workshop on Tropical Cyclones (IWTC-10) report, discusses research findings and operational practices relevant to cyclone types and phase transitions (extratropical, subtropical, and tropical). The cyclone phase space (CPS) method is widely used in both historical investigations and real-time evaluation of cyclone type and transition; however, CPS parameter values depend on input data resolution, and universal thresholds do not currently exist to delineate when a cyclone transitions from one type to another. Assessments of phase transitions in a changing climate highlight potential latitude shifts in extratropical transition and increased potential for tropical transition, but realistic projections of future trends likely require high-resolution simulations that can capture the cyclone warm core.Operational meteorological centers apply varied approaches to cyclone classification via CPS parameters and other criteria, some of which depend on the tropical basin, yet these approaches cannot fully address challenges in operational classification and subsequently in communicating risks associated with these phase transitions. We recommend a multivariate historical assessment of tropical and subtropical cyclones across all basins in which they occur, including the South Atlantic Ocean and the Mediterranean Sea, to identify the potential for a more universal cyclone classification approach that meets operational needs.

Joint inversion for facies and petrophysical properties based on a bi‐level optimization model

AbstractIn many subsurface studies, facies and petrophysical properties are two important reservoir parameters that are closely correlated. They are routinely used in well interpretation, hydrocarbon reserve calculation and production profile prediction. These two parameters have commonly been determined in two separate tasks because of their mathematical differences (facies are discrete, and petrophysical properties are continuous). However, this is incorrect because facies and petrophysical properties are often strongly correlated. Therefore, we propose a new joint inversion method of facies and petrophysical properties based on a bi‐level optimization model. In the bi‐level optimization model, the upper‐level problem is the petrophysical property inversion while the lower‐level problem can identify the facies and add a facies‐related constraint for the upper‐level optimization. We also develop a new genetic algorithm for the discrete‐continuous inversion problem based on the bi‐level optimization model because the inversion problem usually has multiple local solutions. In addition, rock physics and statistics are combined in the inversion process. A rock physics model is used to establish the basic relationships between the petrophysical and elastic parameters, and the statistical approach is used to describe the intrinsic connection among the multiple reservoir parameters based on well log data. The numerical experiments indicate that the traditional separate prediction method and the new joint inversion method can quickly obtain more accurate results. In the application examples of real data, the inversion results can be matched to the well log data within the limits of the input data resolution, which further verifies the reliability and application potential of this new method.

The Histories of Well-Documented Maritime Cyclones as Portrayed by an Automated Tracking Method

Abstract This study examines extratropical cyclone tracks, central pressure, and maximum intensification rates from a widely used automated cyclone tracking scheme and compares them with the manual tracking of five well-known North Atlantic cyclones whose histories are available in the refereed literature. The automated tracking scheme is applied to sea level pressure data from four different reanalyses of varying levels of sophistication to test the sensitivity of the results to input data resolution and quality. Further, we test the tracking scheme using lower-tropospheric vorticity obtained from the most recent reanalysis (ERA5) for four of these cyclone events. Substantial discrepancies in cyclone position, intensity, and maximum intensification rates exist between the manual tracking and the automated tracking and are not eliminated by using higher-resolution reanalyses or by “turning off” the spatial smoothing feature of the automated tracking scheme (needed to reduce spurious cyclone detections). The results point to a particular problem in detecting weaker and earlier stage cyclones and confirm findings from studies that have examined a broad range of cyclone tracking schemes for a range of reanalyses. Notably, this early cyclone stage often involves a smaller-scale secondary cyclogenesis or cyclone wave, which are detected by the automated scheme only after subsequent growth in the ensuing 6–12 h. It is known that these early stages are critical for a comprehensive understanding of rapid intensification events. A discussion of possible future solutions to this problem is presented. Significance Statement Because of the availability of large modern datasets portraying sea level pressure across the globe, meteorologists have turned to automated detection and tracking of midlatitude cyclones. Detection and tracking are of interest since these storm systems play an important role in weather and climate and potential changes in their location, frequency, and intensity are of considerable societal interest given climate change. This paper compares the results obtained from one commonly used automated tracking method with tracks obtained by human analysts. We find substantial discrepancies in cyclone position, intensity, and intensification rates and that these differences are not eliminated by using improved analyses. A discussion of possible future solutions is presented.

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available there remains an unmet need for high-resolution NDVI reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques for NDVI reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer from limited fusion accuracy due to input data quality constraints. To address these limitations this study introduces an innovative Deep Learning-Enhanced Spatiotemporal Fusion Method. Deep learning algorithms are employed to refine the spatial resolution of input data thereby facilitating the separation of complex image elements located at feature boundaries. This improved approach significantly enhances fusion accuracy as validated against five established TSTIF techniques through empirical analysis. As a case study we generate long-term Fractional Vegetation Cover (FVC) datasets to investigate the ecological dynamics of the Danjiang River Basin. Our findings reveal substantial gains in NDVI reconstruction accuracy through the incorporation of deep learning into traditional fusion techniques. Among the methods tested the STRUM algorithm showed the greatest improvement with its R2 value increasing from 0.872 to 0.894 and a consistent reduction in Root Mean Square Error (RMSE). The FSDAF technique emerged as the most effective boasting an R2 value of 0.953 and an RMSE of 0.012. These enhanced NDVI datasets provide more accurate representations of the basin's vegetation cover thus enriching long-term observational archives and facilitating further quantitative remote sensing analyses. Moreover we report a 21-year mean FVC of 0.702 for the Danjiang River Basin characterized by a “north-high south-low” distribution that varies seasonally. Improvements in vegetation cover were observed in Xichuan and Neixiang counties while environmental degradation is evident in the broader Danjiang River watershed. Key factors influencing these spatial variations include elevation and soil composition in addition to climate variables such as rainfall and temperature. Infrastructure projects like the South-to-North Water Diversion Project and targeted ecological conservation policies have also positively impacted vegetation within the basin.

Evaluating Climate Change Effects on a Snow-Dominant Watershed: A Multi-Model Hydrological Investigation

Assessing the impact of climate change on water systems often requires employing a hydrological model to estimate streamflow. However, the choice of hydrological model, process representation, input data resolution, and catchment discretization can potentially influence such analyses. This study aims to evaluate the sensitivity of climate change impact assessments to various hydrological modeling configurations in a snow-dominated headwater system in Alberta, Canada. The HBV-MTL and GR4J models, coupled with the Degree-Day and CemaNeige snowmelt modules, were utilized and calibrated using point- and grid-based climate data on lumped and semi-distributed catchment discretization. The hydrological models, in conjunction with a water allocation model, were supplied with climate model outputs to project changes in the basin. While all models revealed a unanimous increase in peak flow, the difference between their estimations could be as substantial as 42%. In contrast, their divergence was minimal in projecting median flow. Furthermore, most models projected an aggravated water supply deficit between 16% and 40%. Overall, the quantified climate change impacts were the most sensitive to the choice of snow routine module, followed by the model type, catchment discretization, and data resolution in this snow-dominant basin. Therefore, particular attention should be given to the proper representation of snowmelt processes.

Predicting gully erosion susceptibility in South Africa by integrating literature directives with regional spatial data

AbstractGully erosion has been identified as a severe land degradation process with environmental and socio‐economic consequences. Identifying areas susceptible to gully erosion will aid in developing strategies to inhibit future degradation. Various approaches have been implemented to predict and map gully erosion susceptibility but are mostly restricted to small geographical extents because of process limitations. Here, we introduce a novel method that predicts gully erosion susceptibility on a regional/national scale (1.22 million km2) by synthesising literature directives with a statistical approach. Findings from a literature review were used to extract physiographic properties associated with gully erosion that was conditioned to characterise susceptibility by using the Frequency Ratio model. The conditioned physiographic properties were aggregated by a weighted overlay procedure using an aggregation of controlling factors derived from the literature review as a weighting system. The gully susceptibility index (GSI) model was validated against a published gully inventory map (n = 163 019) and randomly generated 1‐km2 tessellation zones from which primary validation data were derived. Although uncertainties within the modelling procedure exist (e.g., gully site distribution, the spatial resolution of input data and determination of gully points), the validation shows that the GSI model is generally robust, identifying areas of contrasting susceptibilities. Furthermore, findings converge with other susceptibility metrics, which have been derived by different methodologies. Because empirical gully erosion research has been conducted worldwide, this model could be applied to regional‐scale gully susceptibility modelling assessments (as a solitary method or combined with primary data) in other parts of the world. Additionally, the GSI model can be adopted to model environmental change scenarios.

Sizing of stationary energy storage systems for electric vehicle charging plazas

Increasing numbers of electric vehicles (EV) and their fast charging stations might cause problems for electrical grids. These problems can be prevented by energy storage systems (ESS). Levelling the power demand of an EV charging plaza by an ESS decreases the required connection power of the plaza and smooths variations in the power it draws from the grid. In this article, a study of sizing of stationary ESSs for EV charging plazas is presented based on one year of data compiled from four direct current fast charging (DCFC) stations. Effects of the charging plaza size, grid connection power, and temporal resolution of input data on ESS requirements were studied. The ESS was controlled to reduce the connection power below certain limits which were altered from 5% to 100% with respect to the nominal power of the charging plaza. The charging plaza size ranged from 1 to 40 DCFC stations. The results show that the relative ESS power and energy requirements and the utilization rate of the ESS decrease, as the connection power and charging plaza size increase. The required connection power of an EV charging plaza can be decreased considerably by a relatively small ESS capacity. The effects of temporal resolution were studied, for the first time, by averaging the charging power time series of the charging plaza with averaging time intervals ranging from 1 s to 1 h. The temporal resolution of EV charging data had a notable effect on the results of ESS sizing: too sparse data distorts the results leading to an underestimation of ESS specifications and utilization.

Input data resolution affects the conservation prioritization outcome of spatially sparse biodiversity features.

Detailed spatial data are an essential part of land use planning and decision-making. Their spatial resolution sets limitations to their use, as coarse datasets are not suitable for detecting small-scale phenomena. In this study, we explored the effects of spatial resolution on the ecological outcome of a conservation prioritization process in Zonation software. Our study area was in Evo, southern Finland, covering a mosaic of managed and conserved forests. We produced the feature layers describing the forest characteristics using high-resolution remote sensing datasets, object-based mapping methods, and forest site type data. We found that increasing the resolution above the 16 m baseline resolution resulted in substantial errors. The conservation errors were the highest for rare features related to European Aspen, whereas the common features related to dominant tree species could benefit from the growth of cell size. We conclude that adequate spatial resolution is a prerequisite for efficient conservation prioritization, and that the size and spatial distribution of the features affect the resolution requirements.

An approach to select optimum inputs for hydrological modeling to improve simulation accuracy in data-scarce regions

Study regionThe source regions of the Yellow River and Yangtze River in the central-eastern part of the Tibetan Plateau. Study focusHydrological model is an important tool in the simulation of watershed hydrology. However, as more and more basic geographic data becomes publicly available and shared globally, researchers are developing a 'symptom' of difficulty in choosing data, so a systematic comparative analysis for model input data selection is necessary. We tested the effects of different types, sources, and resolutions of input data on the model output results based on the SWAT model, and focused on the mechanism of the role of different input data in the model and how to select an appropriate input data for similar studies. New hydrological insights for the regionThe results show that: the meteorological data is crucial in the model's runoff simulation, and ground meteorological observation station data outperforms reanalysis data such as CFSR. Optimizing CFSR significantly improves the model's performance. The DEM resolution minimally impacts runoff simulation, as the difference in results is primarily due to the topographic characteristics of the watershed. DEM selection should consider TWI complexity and its compatibility with the watershed network. The selection of LULC data has little effect on the simulation, and the best input data combination is OBS + 90 m DEM + CNLULC. These findings assist input data selection for similar watersheds using the SWAT model.

Soil Erosion Estimation Using Revised Universal Soil Loss Equation Integrated with Geographic Information System by Different Resolution Digital Elevation Model Data in Weyto Sub-Basin, Southern Ethiopia

Soil erosion is a global environmental challenge for developing countries including Ethiopia that require regular monitoring to take corrective measures. In this context, this study was focused on estimating soil erosion using the Revised Universal Soil Loss Equation (rusle) integrated with Geographical Information System (gis) technique for which it applied 30 m and 200 m resolution Digital Elevation Model (dem) data to generate slope gradient and length. Rainfall erosivity, soil erodibility, land cover/use and management factors data were obtained from existing studies and field-based assessments where the data were used to estimate the soil erosion using rusle model in ArcMap under two different dem resolution scenario. The model estimated an average of 1.38 and 1.86 million tons of annual soil loss by water using 200 and 30 meters resolution dem data, respectively, while keeping other factors constant. The erosion estimated using higher (30 m) resolution dem data was more realistic than low (200 m) resolution data , as the higher resolution dem data allowed less generalization. In high resolution dem data, the slopes generated were also more in line with ground reality. Based on the case study of Weyto sub-basin in Southern Ethiopia, we thus conclude that the gis technique and remote sensing data can be used in rusle based erosion risk prediction for large areas even at basin, sub-basin and macro watershed level. We suggest that the accuracy of the prediction can be improved by using high resolution (large scale) input data disaggregated by micro- and sub-watersheds.