scholarly journals Analysis of hydrological variability over the Volta river basin using in-situ data and satellite observations

2017 ◽  
Vol 12 ◽  
pp. 88-110 ◽  
Author(s):  
Christopher E. Ndehedehe ◽  
Joseph L. Awange ◽  
Michael Kuhn ◽  
Nathan O. Agutu ◽  
Yoichi Fukuda
Keyword(s):  
River Basin ◽  
Satellite Observations ◽  
In Situ Data ◽  
Hydrological Variability

A method for constructing synthetic parallel tracks for augmenting in-situ data with satellite observations

2020 ◽  
Vol 199 ◽  
pp. 105191
Author(s):  
Sreenivas Pentakota ◽  
Seshagiri Rao Kolusu ◽  
Sagar V. Gade ◽  
K. Srinivasu ◽  
J Prithvi Raj
Keyword(s):  
Satellite Observations ◽  
In Situ Data

scholarly journals Adequacy of the In Situ Observing System in the Satellite Era for Climate SST

2006 ◽  
Vol 23 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Huai-Min Zhang ◽  
Richard W. Reynolds ◽  
Thomas M. Smith
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Sea Surface ◽  
Bias Error ◽  
Random Errors ◽  
In Situ Data ◽  
Satellite Sst ◽  
Maximum Bias ◽  
Very High

Abstract A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE). The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

scholarly journals New approach to evaluate satellite derived XCO<sub>2</sub> over oceans by integrating ship and aircraft observations

2020 ◽  
Author(s):  
Astrid Müller ◽  
Hiroshi Tanimoto ◽  
Takafumi Sugita ◽  
Toshinobu Machida ◽  
Shin-ichiro Nakaoka ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Satellite Observations ◽  
Reference Dataset ◽  
Model Calculations ◽  
New Approach ◽  
Mixing Ratios ◽  
In Situ Data

Abstract. Satellite observations provide spatially-resolved global estimates of column-averaged mixing ratios of CO2 (XCO2) over the Earth's surface. The accuracy of these datasets can be validated against reliable standards in some areas, but other areas remain inaccessible. To date, limited reference data over oceans hinders successful uncertainty quantification or bias correction efforts, and precludes reliable conclusions about changes in the carbon cycle in some regions. Here, we propose a new approach to analyze and evaluate seasonal, interannual and latitudinal variations of XCO2 over oceans by integrating cargo-ship (SOOP, Ship Of Opportunity) and commercial aircraft (CONTRAIL, Comprehensive Observation Network for Trace gases by Airliner) observations with the aid of state-of-the art atmospheric chemistry-transport model calculations. The consistency of the in situ based column-averaged CO2 dataset (in situ XCO2) with satellite estimates was analyzed over the Western Pacific between 2014 and 2017, and its utility as reference dataset evaluated. Our results demonstrate that the new dataset accurately captures seasonal and interannual variations of CO2. Retrievals of XCO2 over the ocean from GOSAT (Greenhouse gases observing satellite: NIES v02.75, National Institute for Environmental Studies; ACOS v7.3, Atmospheric CO2 Observation from Space) and OCO-2 (Orbiting Carbon Observatory, v9r) observations show a negative bias of about 1 parts per million (ppm) in northern midlatitudes, which was attributed to measurement uncertainties of the satellite observations. The NIES retrieval had higher consistency with in situ XCO2 at midlatitudes as compared to the other retrievals. At low latitudes, it shows many fewer valid data and high scatter, such that ACOS and OCO-2 appear to provide a better representation of the carbon cycle. At different times, the seasonal cycles of all three retrievals show positive phase shifts of one month relative to the in situ data. The study indicates that even if the retrievals complement each other, remaining uncertainties limit the accurate interpretation of spatiotemporal changes in CO2 fluxes. A continuous long-term XCO2 dataset with wide latitudinal coverage based on the new approach has a great potential as a robust reference dataset for XCO2 and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.

scholarly journals Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

2019 ◽  
Vol 11 (22) ◽  
pp. 2709 ◽  
Author(s):  
Chelsea Dandridge ◽  
Venkat Lakshmi ◽  
John Bolten ◽  
Raghavan Srinivasan
Keyword(s):  
Southeast Asia ◽  
River Basin ◽  
Watershed Modeling ◽  
Rain Gauge ◽  
Mekong River ◽  
Coefficient Of Determination ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Rainfall Estimates

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

scholarly journals Analysis of MODIS LST Compared with WRF Model and in situ Data over the Waimakariri River Basin, Canterbury, New Zealand

2012 ◽  
Vol 4 (11) ◽  
pp. 3501-3527 ◽  
Author(s):  
Mohammad Sohrabinia ◽  
Wolfgang Rack ◽  
Peyman Zawar-Reza
Keyword(s):  
New Zealand ◽  
River Basin ◽  
Wrf Model ◽  
In Situ Data

Groundwater storage change in the Jinsha River basin from GRACE, hydrologic models, and in situ data

2020 ◽  
Author(s):  
Nengfang Chao
Keyword(s):  
Sediment Transport ◽  
River Basin ◽  
Human Activities ◽  
Water Storage ◽  
Hydrologic Models ◽  
Groundwater Storage ◽  
Jinsha River ◽  
In Situ Data ◽  
Storage Change

&lt;p&gt;Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment (GRACE) mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76&amp;#177;0.10 cm/year during the period 2003&amp;#8211;2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change (SMC), GWSC and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change (SWSC) and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming, and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.&lt;/p&gt;

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