Using satellite-based weather data as input to SWAT in a data poor catchment

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.

Precipitation is a vital component of the hydrologic cycle, and successful hydrological modeling largely depends on the quality of precipitation input. Gridded precipitation datasets are gaining popularity as a convenient alternative for hydrological modeling. However, many of the gridded precipitation data have not been adequately assessed across a range of conditions. This study compared three gridded precipitation datasets, Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM). This study used the conventional gauge observation as reference data and evaluated the suitability of the three sources of gridded rainfall data to drive rainfall-runoff simulations. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) were used to create daily streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas, with the TRMM, CFSR, PRISM, and gauge rainfall data used as inputs. A direct comparison of the gridded data sources showed that the TRMM data underestimates the volume of rainfall, while PRISM data most closely matches the volume of rainfall when compared to the gauge rainfall observations. The hydrological simulation results showed that the PRISM and TRMM rainfall data driven models had preferable results to the CFSR and gauge driven models, in terms of both graphical comparison and goodness-of-fit indicator values. Additionally, no significant discrepancy was found between SWAT and ANN simulation results when the same precipitation data source was used, while SWAT and ANN simulation results varied in an identical pattern when different precipitation data sources were applied.

This paper focuses on diagnosing the strength of soil moisture–atmosphere coupling at subseasonal to seasonal time scales over Asia using two different approaches: the conditional correlation approach [applied to the Global Land Data Assimilation System (GLDAS) data, the Climate Forecast System Reanalysis (CFSR) data, and output from the regional climate model, version 4 (RegCM4)] and the Global Land–Atmosphere Coupling Experiment (GLACE) approach applied to the RegCM4. The conditional correlation indicators derived from the model output and the two observational/reanalysis datasets agree fairly well with each other in the spatial pattern of the land–atmosphere coupling signal, although the signal in CFSR data is stronger and spatially more extensive than the GLDAS data and the RegCM4 output. Based on the impact of soil moisture on 2-m air temperature, the land–atmosphere coupling hotspots common to all three data sources include the Indochina region in spring and summer, the India region in summer and fall, and north-northeastern China and southwestern Siberia in summer. For precipitation, all data sources produce a weak and spatially scattered signal, indicating the lack of any strong coupling between soil moisture and precipitation, for both precipitation amount and frequency. Both the GLACE approach and the conditional correlation approach (applied to all three data sources) identify evaporation and evaporative fraction as important links for the coupling between soil moisture and precipitation/temperature. Results on soil moisture–temperature coupling strength from the GLACE-type experiment using RegCM4 are in good agreement with those from the conditional correlation analysis applied to output from the same model, despite substantial differences between the two approaches in the terrestrial segment of the land–atmosphere coupling.

The management of water resources requires adequate information on the quantities of water supplied from the basins that outfall into a river, especially during the flood seasons. The study area located in the western part of Iraq within the administrative boundaries of the Heet district about 70 km from Haditha Dam, 45km from Ramadi in Anbar province. The study aims to evaluate the amount of surface runoff through a long-term period (1981-2019). Soil and Water Assessment Tool (SWAT) related to Geographic Information System (ArcGIS) was used for the simulation. The input data was the Digital Elevation Model (DEM) of SRTM with resolution 30m, land use/land cover map from the European Space Agency (ESA) with resolution 300m and, soil map from the Food and Agriculture Organization (FAO). The weather data used in the study were obtained from the Climate Forecast System Reanalysis (CFSR) combined with the weather data from the Surface meteorology and Solar Energy (SSE) produced by NASA. These weather data prepared using SWAT weather database software to be ready for the simulation processes. Al-Mohammedi valley was calibrated and validated using SWAT-CUP software using the available recorded discharges at Heet, Ramadi, and Al-Warar gauge stations. The calibration is based on the meteorological data for the period January 1, 2002, to December 31, 2006, and the validation was based on the data between January 1, 2007, to December 31, 2009. The model calibration and validation results based on two objective functions "Nash-Sutcliffe (NS) and coefficient of determination(R2)" showed that SWAT was successfully simulated Al-Mohammedi valley with NS = 0.72 and R2 = 0.76 for calibration, and NS = 0.63 and R2 = 0.65 for validation. According to SWAT results, the average runoff volume in the long-term period of simulation from January 1, 1981, to October 31, 2019, was 79.2 million m3 while the average runoff depth was 18.25 mm with about 17 % of rainfall becomes surface runoff.

Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS), and the Climate Forecast System Reanalysis (CFSR) in the Qinghai-Tibet Plateau (TP), were evaluated in this paper. The accuracy of daily precipitation, max/min temperature, relative humidity and wind speed from CMADS and CFSR are firstly evaluated by comparing them with results obtained from 131 meteorological stations in the TP. Statistical results show that most elements of CMADS are superior to those of CFSR. The average correlation coefficient (R) between the maximum temperature and the minimum temperature of CMADS and CFSR ranged from 0.93 to 0.97. The root mean square error (RMSE) for CMADS and CFSR ranged from 3.16 to 3.18 °C, and ranged from 5.19 °C to 8.14 °C respectively. The average R of precipitation, relative humidity, and wind speed for CMADS are 0.46; 0.88 and 0.64 respectively, while they are 0.43, 0.52, and 0.37 for CFSR. Gridded observation data is obtained using the professional interpolation software, ANUSPLIN. Meteorological elements from three gridded data have a similar overall distribution but have a different partial distribution. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrological processes in the Yellow River Source Basin of the TP. The Nash Sutcliffe coefficients (NSE) of CMADS+SWAT in calibration and validation period are 0.78 and 0.68 for the monthly scale respectively, which are better than those of CFSR+SWAT and OBS+SWAT in the Yellow River Source Basin. The relationship between snowmelt and other variables is measured by GeoDetector. Air temperature, soil moisture, and soil temperature at 1.038 m has a greater influence on snowmelt than others.

Hydrological modelling requires accurate climate data with high spatial-temporal resolution, which is often unavailable in certain parts of the world—such as Central America. Numerous studies have previously demonstrated that in hydrological modelling, global weather reanalysis data provides a viable alternative to observed data. However, calibrating and validating models requires the use of observed discharge data, which is also frequently unavailable. Recent, global-scale applications have been developed based on weather data from reanalysis; these applications allow streamflows with satisfactory resolution to be obtained. An example is the Global Flood Awareness System (GloFAS), which uses the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5) as input. It provides discharge data from 1979 to the present with a resolution of 0.1°. This study assesses the potential of GloFAS for calibrating hydrological models in ungauged basins. For this purpose, the quality of data from ERA5 and from the Climate Hazards Group InfraRed Precipitation and Temperature with Station as well as the Climate Forecast System Reanalysis (CFSR) was analysed. The focus was on flow simulation using the Soil and Water Assessment Tool (SWAT) model. The models were calibrated using GloFAS discharge data. Our results indicate that all the reanalysis datasets displayed an acceptable fit with the observed precipitation and temperature data. The correlation coefficient (CC) between the reanalysis data and the observed data indicates a strong relationship at the monthly level all of the analysed stations (CC > 0.80). The Kling–Gupta Efficiency (KGE) also showed the acceptable performance of the calibrated SWAT models (KGE > 0.74). We concluded that GloFAS data has substantial potential for calibrating hydrological models that estimate the monthly streamflow in ungauged watersheds. This approach can aid water resource management.

Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia

ABSTRACTThe mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33‐year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis products. Mean 80‐m wind speeds are consistently higher over China Seas and the ocean areas than over land, and inside China high winds are found in areas of Inner Mongolia and the Tibetan Plateau. There is a considerable seasonal variability that reflects primarily the influence of East and Southeast Asia Monsoon with generally higher speeds in winter followed by summer, and weaker winds in autumn, followed by spring. There is also a strong interannual variability, and regions of larger amplitude of variability coincide with regions of higher mean winds. A decreasing trend, dominated by a sharp decline beginning in 2005, is seen across China and the surrounding seas in summer and autumn, and the summer trends over land and over ocean appear to be related respectively to the Pacific Decadal Oscillation (PDO) and the East Asian summer monsoon. Trends are not consistent across the region in spring and winter, however, with positive trends over some areas in northeastern and northwestern China, Mongolia and tropical oceans whereas negative trends in other regions. Nearly all areas of China experience mean annual 80‐m wind speed less than 6.9 m s−1 (wind power classes of 1–2) except for some areas of Inner Mongolia where mean annual 80‐m wind speeds exceed 6.9 m s−1 (Classes 3 or higher, suitable for wind energy development). China Seas and ocean areas generally fall in Class 3 or above, with the Taiwan and Luzon Straights reaching the highest Class 7.

This chapter evaluates the impacts of projected climate change (period: 2020–2100) on the hydrologic systems along the Eastern Red Sea coastal plain. Shuttle Radar Topography Mission (SRTM) data and Landsat Thematic Mapper (TM) scenes were used to delineate watersheds and their channel networks (155 watersheds) that collect precipitation over the Red Sea Hills in the Kingdom of Saudi Arabia (KSA) and drain toward the Red Sea coastal plain. Continuous rainfall-runoff models were constructed and calibrated for each of the 19 major watersheds (watershed area: 1,825–107,769 km2; total area: 176,683 km2). Regionalization techniques were applied to extrapolate catchment-specific parameters from proximal calibrated catchments (i.e., Wadi Girafi, which has an area of 5,057 km2). The calibrated Soil Water and Assessment Tool (SWAT), and climatic inputs from the Climate Forecast System Reanalysis (CFSR) database, and from Tropical Rainfall Measuring Mission (TRMM) measurements were used to calculate the partitioning (runoff, evapotranspiration, and potential groundwater recharge) of precipitation for the time period from 1998 to 2010. TRMM data were validated by comparing them to rain gauge data (period: 1998–2012; number of rain gauge stations: >100) across the study area. Downscaled outputs of the Community Climate System Model (CCSM4) were used to simulate (for the period 2020–2100) the effects of climatic changes on the selected 19 Red Sea watersheds. Findings indicate that (1) the average annual precipitation is 130 mm and the total annual amount is 23 × 109 m3; (2) with time, precipitation will increase over the northern and central watersheds and decrease over the southern watersheds; and (3) global warming is causing a rise in sea surface temperature, enhancing evaporation, intensifying northerly and northwesterly winds and precipitation over the northern and central watersheds, and possibly causing shifts in monsoonal fronts and modifying precipitation patterns over the southern watersheds.KeywordsShuttle Radar Topography MissionSoil Water Assessment ToolTropical Rainfall Measuring MissionSoil Water Assessment Tool ModelClimate Forecast System ReanalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

The study has shown the integration of Arc SWAT with Arc GIS and remote sensing tool are helpful analyze and evaluate both spatial and temporal land use/cover dynamics. It has also shown that Arc SWAT is an effective tool in analyzing the impacts of land use/cover changes on stream flow in areas with limited readily available data. This study is mainly focusing on the investigation of the impacts of land use / land cover changes on the stream flow of Muga watershed which is located in the East Choke Mountains watersheds, Upper Abbay Basin, East Gojjam Zone, Amhara Regional state, Ethiopia. Soil and Water Assessment Tool (SWAT) model were used it investigate the impact of land cover change on the stream flow. For this study SWAT Simulation is used in identifying the most vulnerable sub basins to the stream flow and sediment load changes of Muga watershed. The model was calibrated and validated using historic Stream flow data. The model was calibrated using stream flow data from 1993 to1998, validated from 1999 to2002. The R 2 and NSE values were used to examine model performance and the result indicates 0.81 and 0.87 to R 2 and 0.80 and 0.86 to NSE during calibration and validation respectively. The result of this analysis indicated that the mean monthly stream flow for wet months had increased by 17.75m 3 /s while the dry season decreased by 12.76m 3 /s during the 1995-2013 period due to the land use and land cover change. The highest annual surface runoff was attributed by sub basin 5 whereas sub basin 6 contributes the highest ground water respectively for 1995, 2003 and 2013 land cover maps. In terms of sediment yield, sub basin 1 contributes a maximum load for the study periods. Keywords : Arc SWAT, Arc GIS, Land Use /land cover change, Muga Watershed DOI: 10.7176/CER/13-2-01 Publication date: April 30 th 2021

Improving hydrological simulation in the Upper Mississippi River Basin through enhanced freeze-thaw cycle representation

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

A simulation study using the Soil and Water Assessment Tool (SWAT) model has been initiated to assess current and alternative nutrient, cropping, and management practices in the Sani River Basin in an effort to help mitigate climate change impact in the basin. The SWAT software system has been constructed to manage the input and output data for the SWAT simulations, and the execution of the model for each scenario. An overview of the modeling structure that has been developed for the basin is presented, including the spatial representation of the region within SWAT. Initial results for the baseline simulation are also presented for monthly stream flows near the outlet of the basin, and for annual stream flows for the basin. Overall, the model demonstrated good performance in capturing the patterns and trend of the observed flow series, which confirmed the appropriateness of the model for future scenario simulation. Therefore, SWAT model can be taken as a potential tool for simulation of the hydrology of watershed which behaves hydro-meteorologically similar with Sani basin. Future studies on Sani watershed modeling should address the issues related to water quality and evaluate best management practices.

Multiple oceanic eddies coexist in the North Pacific subtropical front zone (STFZ) in winter, which can be classified into the isolated single eddies, the combined double isotropic eddies and pairs of anisotropic eddies. The forcings of these eddies on the mid-latitude atmosphere are investigated using Climate Forecast System Reanalysis (CFSR) data from year 1979 to 2009, which are divided into the remote and local effects in this research. In the years with a stronger subtropical front, there are more cyclonic isolated and double eddies to the north, more anticyclonic isolated and double eddies to the south of the STFZ, and more eddy pairs with cold to the north and warm to the south concentrated around the main axis of the STFZ. These eddy distributions enhance the strength of the subtropical front, intensify the propagation of upwards baroclinic waves in the lower atmosphere, and finally enhance the zonal wind at upper atmosphere, which is defined as the remote effects of the eddies. However, distinct from this basin-scale remote forcings, three types of oceanic eddies also have different local forcings on the marine atmospheric boundary layer (MABL) above and on the middle atmosphere as expressed in local precipitation difference. The local effects of the isolated single eddies and combined double isotropic eddies take place near the eddy center, whereas that of the pairs of anisotropic eddies at the boundary of the two eddies.