Climate Forecast System Reanalysis Data Research Articles

Assessing the suitability of CFSR data for SWAT model hydrologic simulation of Kunthipuzha river basin, Kerala, India

Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

The Influences of the Multi‐Scale Sea Surface Temperature Anomalies in the North Pacific on the Jet Stream in Winter

AbstractUsing Climate Forecast System Reanalysis (CFSR) data and numerical simulations, the impacts of the multi‐scale sea surface temperature (SST) anomalies in the North Pacific on the boreal winter atmospheric circulations are investigated. The basin‐scale SST anomaly as the Pacific Decadal Oscillation (PDO) pattern, a narrow meridional band of frontal‐scale smoothed SST anomaly in the subtropical front zone (STFZ) and the spatial dispersed eddy‐scale SST anomalies within the STFZ are the three types of forcings. The results of statistical methods find that all three oceanic forcings may correspond to the winter North Pacific jet changing with the similar pattern. Furthermore, several atmospheric general circulation model simulations are used to reveal the differences and detail processes of the three forcings. The basin‐scale cold PDO‐pattern SST anomaly first causes negative turbulent heat flux anomalies, atmospheric cooling, and wind deceleration in the lower atmosphere. Subsequently, the cooling temperature with an amplified southern lower temperature gradient and baroclinity brings a lagging middle warming because of the enhanced atmospheric eddy heat transport. The poleward and upward development of baroclinic fluctuations eventually causes the acceleration of the upper jet. The smoothed frontal‐ and eddy‐scales SST anomalies in the STFZ cause comparable anomalous jet as the basin‐scale by changing the upward baroclinic energy and Eliassen‐Palm fluxes. The forcing effects of multi‐scales SST anomalies coexist simultaneously in the mid‐latitude North Pacific, which can cause similar anomalous upper atmospheric circulations. This is probably why it is tricky to define the certain oceanic forcing to the specific observed atmospheric circulation variation.

Compensation between Resolved Wave Forcing and Parameterized Orographic Gravity Wave Drag in the Northern Hemisphere Winter Stratosphere Revealed in NCEP CFS Reanalysis Data

Abstract Compensation between the resolved wave (RW) forcing and the parameterized orographic gravity wave drag (OGWD) accompanying barotropic/baroclinic (BT/BC) instability in the realistic atmosphere is investigated using Climate Forecast System Reanalysis data in the Northern Hemisphere winter stratosphere. When sufficiently narrow and/or strong negative OGWD drives instability, RWs are generated in situ, providing positive Eliassen–Palm flux divergence that compensates for the parameterized OGWD enhancement; this is consistent with the findings of previous studies based on the idealized general circulation models. However, dependence of the compensation rate on RW forcing differs from the nearly complete compensation in the previous studies, implying that an additional mechanism operates for the compensation: the refractive-index modification by BT/BC instability. The negative meridional gradient of the quasigeostrophic potential vorticity leads to the negative refractive index squared for RWs with phase speeds less than the zonal-mean zonal wind. This prevents RWs from entering the destabilized areas, resulting in the divergence of Eliassen–Palm fluxes that cancels out the parameterized OGWD perturbation. Although both mechanisms act simultaneously, the refractive-index modification plays an important role in the compensation processes in the stratosphere where RWs are dominated by the planetary-scale waves.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Hydrological evaluation of gridded climate datasets in a texas urban watershed using soil and water assessment tool and artificial neural network

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.

Development of a hybrid wind field for modeling the tropical cyclone wave field

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters.

Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pastureland types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 ± 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (−1.53 Pg C) and the lowest with the MERRA (−2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 ± 0.06 Pg C from 1981–2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia.

An Evaluation of the Extreme Rainfall Event of 2010 over the Kabul River Basin using the WRF Model

Extreme precipitation events are among the most severe weather hazards. Knowledge about the spatial patterns underlying such events in the Upper Indus Basin is limited because estimating precipitation is very challenging due to the data scarcity and the complex orography. Numerical weather prediction models can be applied at a fine resolution to overcome this issue. The Advanced Research Weather Research and Forecasting (WRF) model version 3.8.1 was applied over the Kabul River Basin to simulate the temperature and precipitation of monsoon season 2010, i.e., 1st May to 16th September 2010. We considered the May month as a spin-up period. The initial and boundary conditions were derived from the National Oceanic and Atmospheric Administration, Climate Forecast System Reanalysis data. The model was set up by using two-nested domains with increasing horizontal resolution moving inward from 15km on domain d01 to 5km on domain d02. The simulations were compared with TRMM 3B42, and station data collected from the Pakistan Meteorological Department and Water and the Power Development Authority using bias, percentage bias, root mean square error, and Pearson correlation. The results revealed that the simulated precipitation was improved from d01 to d02. However, the model showed mixed results with overestimation of precipitation at some stations and underestimations at others. Simulated precipitation generally agreed better with TRMM than with station data. Overall, the results indicate that the WRF model can be used to simulate heavy precipitation in complex terrain.

Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction

Climate change potential impacts are evaluated through the changes in the local and regional climate. However, Global and Regional Climate simulated outputs do not often capture these changes well, hampering their direct applicability. Impact studies using coarse resolution data require bias-correction of climate variables, a process that minimizes the discrepancy between observed and simulated climate variables. This study assessed climate projections in the Volta Basin using an ensemble of 4 Regional Climate Models under the Representative Concentration Pathways-RCPs 4.5 and 8.5 scenarios in the CORDEX-Africa datasets. These datasets were bias-corrected using the Climate Model data for hydrologic modeling tool (CMhyd) and 27-years of Climate Forecast System Reanalysis data. The performances of the ensemble bias-corrected precipitation ranged from 97-99%, 93-99%, 70-485mm, and -9-5% for R2, NSE, RMSE, and PBIAS respectively. TMAX bias-correction performances ranged from 65-99%, 27-99%, 0-4°C and -2-7% for R2, NSE, RMSE and PBIAS respectively. For TMIN, the performances ranged from 91–99%, 91–99%, 0-1°C and 0-1% for R2, NSE, RMSE and PBIAS respectively. The annual projected change in precipitation under RCP4.5 and 8.5 indicated a decrease in precipitation for the near (the 2020s), the mid-century (2050s), and the end far (2080s) with a relative increase from late November to January, a period currently part of dry season period in the Volta Basin. This suggests that the basin could expect a potential shift in the rainy season. The 12-month standard precipitation index suggests more frequent and longer drought periods in the future. Changes in annual mean monthly maximum temperature revealed an increase under all scenarios and throughout the century with an intensified increase by the end of the century under the higher CO2 concentration scenario (RCP 8.5). The study showed that under RCP 4.5 and 8.5 scenarios, the Volta Basin will experience frequent drought and extreme precipitation events, warmer days, and nights temperatures although RCP 4.5 showed a relatively lower magnitude of these extremes. It is therefore important to emphasize the need for strong adaptation to preserve water resources, limit negative impacts on energy and agricultural production, and other ecosystems services in the Volta Basin.

Identification of Rainfall and Inundation pattern using remotely sensed data in Lake Sentarum Floodplain Area

Wetlands are vulnerable natural habitats that should be preserved to protect habitat for fish and wildlife, flood mitigation, improve water quality, recharge area, and maintain surface water flow during dry periods. Water bodies and swamp areas are two primary components of the wetland. Considering its essential roles for the ecosystem, Lake Sentarum was set as a national park area (Lake Sentarum National Park – TNDS), Indonesia's 15 national priority lakes, and; designated as a Ramsar site (The Convention on Wetlands) in Indonesia. Despite the significant roles for the ecosystem, providing the limnological characteristic of Lake Sentarum remains a challenge due to its remote location. This study aims to identify the rainfall and inundation characteristics in the Lake Sentarum area and develop the rainfall-inundation relationship in the TNDS area. First, we carried out rainfall analysis using the Climate Forecast System Reanalysis (CFSR) data. Second, we utilized a remote-sensing-based global surface water map from the Joint Research Centre (JRC) to describe the historical inundation pattern. Third, we applied the Normalized Different Water Index (NDWI) combined with Modification Normalized Different Water Index (MNDWI) to the selected Landsat dataset to extract the inundation area. Finally, we developed a rainfall-inundation relationship in the TNDS area. The result indicated that the yearly rainfall in the TNDS area has an increasing trend, with the highest peak in December and the second peak in April. Historical Landsat data shows that the TNDS has a complex pattern of inundation. The maximum water extent was 649 km2, with a 95 km2 as permanent (90>- 100 % water occurrence). These areas were constantly flooded, even in the dry season. The most significant non-permanent water was 161 km2 (80>- 90 % water occurrence). This permanent and larger temporary water area provides fish and other aquatic biotas habitats. It temporarily stores the water flowing slowly into the River Kapuas through the Tawang River. We captured the spatial inundation pattern and its relationship with the temporal regional rainfall. The developed relationship showed a lag of -60 days of accumulated rainfall correlated with the inundation area (R2 of 0.48, n=11). These findings will thus provide valuable data for lake managers and policy-makers to protect the biota and habitat in Lake Sentarum National Park area.

Local and remote forcing effects of oceanic eddies in the subtropical front zone on the mid-latitude atmosphere in Winter

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.

Water availability trends across water management zones in Uganda

AbstractThis study assessed trends in gridded (0.25° × 0.25°) Climate Forecast System Reanalysis (CFSR) precipitation, potential evapotranspiration (PET), and precipitation minus PET (PMP) across the four water management zones (WMZs) in Uganda including Kyoga, Victoria, Albert, and Upper Nile. The period considered was 1979–2013. Validation of CFSR datasets was conducted using precipitation observed at eight meteorological stations across the country. Observed precipitation trend direction was satisfactorily reproduced by CFSR data extracted at five out of eight stations. Negative (positive) values of long‐term PMP mean were considered to indicate areas characterized by water scarcity (surplus). Areas with large positive PMP were confined to Lake Victoria and mountains such as Rwenzori and Elgon. The largest negative PMP values were in the arid and semi‐arid areas of north and northeastern Uganda. The null hypothesis H0 (no trend) was rejected (p < 0.05) for increasing annual precipitation trends across the various WMZs except in the extreme eastern parts of the Upper Nile, Kyoga, and Victoria WMZs (or areas along the boundary of Uganda and Kenya). The H0 (no trend) was rejected (p < 0.05) for decreasing trends in annual PET over West Nile region of the Upper Nile, western parts of Victoria, and the Albert WMZs. For increasing trend in PMP, the H0 (no trend) was rejected (p < 0.05) across the various WMZs except around the Mount Elgon area. The study findings are relevant for planning of water resources management across the different WMZs in the country.

Spatial‐temporal evaluation of different reference evapotranspiration methods based on the climate forecast system reanalysis data

AbstractEvapotranspiration is a major component of the interaction between land‐surface processes and the atmosphere. Climate Forecast System Reanalysis (CFSR) data offer a promising database for overcoming the limitations in availability and reliability of climatological data and, hence, for understanding the evapotranspiration process. Using these data on grid‐by‐grid daily, seasonal and yearly scales, the present study attempts to advance the spatio‐temporal evaluation of two radiation‐based and three temperature‐based methods for estimating potential evapotranspiration (PET) against estimates of grass reference evapotranspiration (ETo) by FAO Penman–Monteith method (FAO‐PM). The analysis was performed for the period 1979–2013, considering the second largest (79 000 km2) river system in Ethiopia, that is, Omo‐Gibe basin, which accommodates national parks and vast hydropower, cultivation and afforestation developments and discharges its flow to Lake Turkana in Kenya. Despite the large regional variations in climate and elevation, the results in overall emphasize the outperformance of the simple temperature method, viz. Hargreaves–Samani method, in capturing both the annual and seasonal FAO‐PM estimates. Calibration of the Hargreaves–Samani equation is, however, a requisite for spectacular improvement of its performance. Accordingly, new coefficients of the equation are proposed. The annual trends in the basin's ETo increased with rising temperature and decreasing relative humidity, wind speed, and solar radiation, but with decreasing (increasing) rainfall in the upper region (the middle and lower regions). It is deduced that trends in simple methods do not necessarily reflect the true trends in ETo. Annual ETo decreases with increasing elevation and annual rainfall. The present findings are discussed in the context of a worldwide literature, thereby improving the understanding of the best performing PET methods in similar data‐scarce national or transboundary rivers basin in Ethiopia, the region or worldwide. The wider implications regarding water loss from reservoirs and the rain‐fed food and sugar production in the basin under study are also highlighted.

Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam

To improve knowledge of this matter, the potential application of two gridded meteorological products (GMPs), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR), are compared for the first time with data from ground-based meteorological stations over 6 years, from 2008 to 2013, over the Cau River basin (CRB), northern Vietnam. Statistical indicators and the Soil and Water Assessment Tool (SWAT) model are employed to investigate the hydrological performances of the GMPs against the data of 17 rain gauges distributed across the CRB. The results show that there are strong correlations between the temperature reanalysis products in both CMADS and CFSR and those obtained from the ground-based observations (the correlation coefficients range from 0.92 to 0.97). The CFSR data overestimate precipitation (percentage bias approximately 99%) at both daily and monthly scales, whereas the CMADS product performs better, with obvious differences (compared to the ground-based observations) in high-terrain areas. Regarding the simulated river flows, CFSR-SWAT produced “unsatisfactory”, while CMADS-SWAT (R2 > 0.76 and NSE > 0.78) performs better than CFSR-SWAT on the monthly scale. This assessment of the applicative potential of GMPs, especially CMADS, may further provide an additional rapid alternative for water resource research and management in basins with similar hydro-meteorological conditions.

Projected climate change impacts on soil erosion over Iran

In the past few decades, there has been increasing interest in studying various environmental phenomena derived from climate change and its social, environmental, and economic serious impacts. It is particularly critical to developing countries where resources are limited, and conditions may worsen in the midst of environmental disasters caused by soil erosion, leading to indispensable costs in resources and human lives. Since no studies have investigated these implications on a regional scale for the case of Iran, here we assessed the impact of climate change on soil erosion across the country. The RUSLE model was employed to estimate potential soil erosion based on historical climate records and projected future data to assess climate change implications. Daily rainfall data (1987–2006) from 103 meteorological stations were used to bias correct the Climate Forecast System Reanalysis (CFSR) rainfall data. Furthermore, CFSR data, as the historical record period data (1987–2006), was used for bias correcting five General Circulation Models (GCMs) to develop rainfall erosivity values for the future climate period (2046–2065) under SSP2-4.5 and SSP5-8.5 scenarios. Based on these data sets, rainfall erosivity maps were developed to compare the variations with projected climate results across Iran. Results show an overall decrease in rainfall erosivity and soil erosion in most provinces. Considering the average outputs of five GCMs and under both SSPs, the most affected provinces with projected soil erosion decrease are P.11 and P.14, whereas the highest increase in soil erosion is likely to occur in P.13 by SSP5-8.5. In the northern provinces, particularly in P.7, the highest increase in soil erosion is expected. It was also noted that the highest range of soil erosion change among the five selected GCMs occurs at P.27, with the maximum increase of above 135% and the minimum decrease of 71% from the historical period. The present study represents the first assessment of soil erosion at a large scale in Iran, providing an overview of soil erosion that may be useful for soil and water conservation planning, hazard mapping, agriculture, and other activities that are subject to soil erosion.

Evaluation of the Climate Forecast System Reanalysis data for hydrological model in the Arctic watershed Målselv

Abstract The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were performed by the Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm in the SWAT Calibration Uncertainties Program for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrates its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data is a reliable source for running hydrological models in the Arctic watershed Målselv.

Effects of nonlinear terms and topography in a storm surge model along the southeastern coast of China: a case study of Typhoon Chan-hom

Based on Finite Volume Coastal Ocean Model (FVCOM), this study constructed a numerical model covering the Bohai Sea, Yellow Sea, and East China Sea. National Centers for Environmental Prediction’s Climate Forecast System Reanalysis (NCEP-CFSR) data were used to drive the model to simulate a large storm surge generated by Typhoon Chan-hom. The model was validated by multiple in situ observations of water levels taken at tidal gauge stations. The effects of the nonlinear terms and topography on the modeling of storm surges were then studied. First, the tide–surge interaction during the storm surge process was analyzed. The results show that the tide–surge interaction can suppress the storm surge at the climax of the astronomical tide and benefit the growth of the storm surge at the ebb of the astronomical tide. The tidal constituents M2, S2 and K1 were added to analyze the influences of the amplitudes and periods of tides on the nonlinear reaction. The results indicate that the nonlinear effect will be enhanced by an increased tidal height; additionally, the nonlinear interaction of semidiurnal tides is more significant than that of diurnal tides. The semidiurnal fluctuation that appears near the peak-value time is also attributed to the tide–surge interaction. Moreover, the tide–surge interaction can be influenced by the water depth and position of the area of interest. According to the numerical results, topography has a certain impact on the storm surge: The peak value of the storm surge will decrease with increasing slope. The existence of the Ryukyu Islands reduces the area influenced by the storm surge on the southeast coast of China but expands the high-value storm surge area.

Simulation effect evaluation of single-outlet and multi-outlet calibration of Soil and Water Assessment Tool model driven by Climate Forecast System Reanalysis data and ground-based meteorological station data – a case study in a Yellow River source

Abstract It is the research hotspot in the field of hydrology to apply the climate model and its downscaling data into hydrological simulations, and it is very important to evaluate the accuracy of its data. In this study, the accuracy of Climate Forecast System Reanalysis (CFSR) was evaluated from two perspectives: statistical evaluation and hydrological evaluation. In the hydrological evaluation, the applicability of CFSR in the Soil and Water Assessment Tool (SWAT) model of the Yellow River source area was studied. The results show that CFSR temperature data at the source of the Yellow River is consistent with the measured temperature data, and CFSR precipitation data overestimates precipitation. In the Yellow River source runoff simulation, the SWAT model driven by CFSR can obtain satisfactory simulation results. It does not reduce the simulation accuracy at the total outlet of the basin under the multi-outlet calibration method. It also considers the spatial differences of hydrological characteristics of each sub-basin and improve the simulation accuracy of the sub-basin simulation.

A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

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.