Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) final-product V06B with a ground-radar product, Multi-Radar Multi-Sensor (MRMS), over eastern United-States during the hurricane days that occurred in 2016-2018 using both pixel-based and object-based approaches. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS in comparison with the temporally averaged MRMS data (MRMS-Averaged) with a bias reduction of 75% and 65%, respectively. PMW observations tend to show storms with smaller areas in the IMERG final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1mm/hr) in the object-based approach, hurricane objects in the IMERG final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates in the IMERG final product have smaller areas with higher average intensity during the PMW observations compared to data estimated by Morph or IR (morph/IR) observations. It is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. The quality of IMERG data changes with the longer absence of the PMW observations. IMERG data estimated by morph/IR observations, with a 30-minute time-distance to the nearest PMW observation, showed the best agreement with MRMS-Averaged even in comparison with PMW estimates, possibly due to the time-lag in recording the precipitation between satellites and ground-radars. It is also possible to be related to the homogenizing nature of morphing technique in IMERG and averaging MRMS data in time in MRMS-Averaged, relaxing the differences between PMW observations and MRMS. However, the morph/IR data quality deteriorates with the longer absence of PMW sensors. The inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.
How to cite:
Ayat, H., Evans, J., and Behrangi, A.: The effect of different contributing sensors in IMERG-Final precipitation estimates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3575, https://doi.org/10.5194/egusphere-egu21-3575, 2021.
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Hamza Ouatiki, Abdelghani Boudhar, and Abdelghani Chehbouni
Accurate rainfall measurements are crucial for hydrologic modeling. They are mainly provided by rain gauges (RGs), which cover only limited areas. Thus, the gauging network density and distribution can be real constraints in water-related studies, particularly in semi-arid regions. This is the case of Ait-Ouchene and Tilouguite, two mountainous sub-watersheds of the Oum-Rr-Rbia river basin, located in Morocco. Several freely available Spatial Rainfall Products (SRP), with quasi-global coverage, provide rainfall estimates that can constitute a potential complement to the RGs. In this context, we intend to investigate the suitability of eight SRPs (ARC2, CHIRPSp25km, CHIRPSp5km, CMORPH-CRT-V1, GPM-IMERG-V6, PERSIANN-CDR, RFE2, and TRMM-3B42-V7) for daily streamflow simulation in Ait-Ouchene and Tillouguite for the period 2001-2010. We proceeded by a pixel-wise and watershed-wise comparison against data of twenty-six RGs in Oum-Rr-Rbia, using the PCC (Pearson Correlation Coefficient), RMSE, Bias, POD (Detection Probability), and FAR (False Alarms Ratio) metrics. Then, the SRPs were used to annually calibrate the HBV conceptual rainfall-runoff model in Ait-Ouchene and Tilouguite. The SRP-driven simulations’ accuracy was assessed against the gauged streamflow using the NSE metric.
Primarily, the model was tested in Ait-Ouchene through cross-validation, parameter sensitivity, and parameter interdependency analyses, using the RG and MODIS-SCA observations. The results showed that the HBV model can fairly reproduce the observed streamflow, with year-to-year variable reliability. Additionally, the hydroclimatic changes appeared to actuate the model parameters’ interdependency. The latter were found to combine either to shrink the storage capacity of the model’s reservoirs under extremely high streamflow or enlarge them under overestimated water supply, mainly from snow cover. Thus, the snowmelt sub-routine was deactivated, during the evaluation process, to avoid the SWE compensating the bias in the SRP estimates.
Regarding the SRPs evaluation, the rainfall estimates performed relatively poorly for both direct comparison and hydrologic modeling. Most SRPs yielded PCCs below 0.5, except for IMERG and RFE. They exhibited PCCs between 0.54-0.62 (IMERG) and 0.47-0.71 (RFE) at 50% of the RGs, with IMERG performing the best at eighteen out of the twenty-six RGs. IMERG prevalence was also observed in terms of detection capacity showing the highest PODs alongside PERSIANN. The SRPs detected many rainfall events as false alarms, with median FARs greater than 0.52. However, an analysis, where we considered only the grid-cells encompassing more than one RG, revealed that a portion of the false alarms were rainfalls that fell in the RGs’ vicinity. Moreover, the rainfall estimates were substantially biased, where the large rainfall totals were predominantly underestimated. For streamflow simulation, the SRPs’ performance seemed unsteady and varied depending on years and products. While IMERG and RFE frequently produced the best NSEs, CMORPH consistently showed the weakest results. In addition to the important bias contained in the SRP estimates, the low performance in hydrologic modeling can be related to the abundance of insignificant false alarms. Nevertheless, the SRPs provided better streamflow estimates than the RGs in Tillouguite, which has an unevenly distributed gauging network concentrated near the outlet.
How to cite:
Ouatiki, H., Boudhar, A., and Chehbouni, A.: The Suitability of Eight Spatial Rainfall Products to Simulate Daily Streamflow in Semi-Arid Watersheds using the HBV model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7428, https://doi.org/10.5194/egusphere-egu21-7428, 2021.
Near surface air temperature is a key variable used in wide range of applications showing environmental conditions across the earth. Standard meteorological observations generally provide the best estimation with high accuracy over time for a small area of influence. However, considerable uncertainty arises when point measurements are extrapolated or interpolated over much larger areas. Satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. Thus, spatial patterns of air temperature can be derived from satellite remote sensing.
In this study, we evaluate the performance of several satellite-based products of near surface air temperature to determine the best product in estimating daily and monthly air temperatures. Era5 Land, SMAP Level 4, AgERA5, MERRA2 products are used with 1120 ground-based gauge stations for the period 2015-2019 over complex terrain and different climate classes according to Köppen-Geiger climate classification in Turkey. Moreover, several traditional and more sophisticated machine learning downscaling algorithms are applied to increase products’ spatial resolution. The agreement between ground observations and the different products and the downscaled temperature product is investigated using a set of commonly used statistical estimators of mean absolute error (MAE), correlation coefficient (CC), root-mean-square error (RMSE) and bias.
Performance analysis of satellite-based air temperature products with ground-based observations on monthly time series has shown that ERA5 Land and SMAP L4 products have similar capabilities. However, analysis on daily time series depicted that ERA5 Land is superior to SMAP L4 product. Results indicate that bicubic interpolation performs best on downscaling Era5 Land product daily time series. However, Random Forest algorithm is superior on monthly time series.
How to cite:
Karaman, Ç. H. and Akyurek, Z.: Evaluation of gridded near surface air temperature datasets across complex terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14352, https://doi.org/10.5194/egusphere-egu21-14352, 2021.
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Kristen Whitney, Enrique Vivoni, Theodore Bohn, Zhaocheng Wang, Mu Xiao, and Giuseppe Mascaro
The Colorado River Basin (CRB) has experienced widespread and prolonged drought in the 21st century with recent precipitation (P) up to 25% below historical means and air temperature (T) up to 0.8 oC warmer. The extent that continued warming will lead to streamflow (Q) decline is unclear given the high interannual variability of P. Here we explore physically plausible ways that climate change could impact Q using the Variable Infiltration Capacity (VIC) model. We integrated advances in VIC using Landsat- and MODIS-based products to produce more realistic land surface conditions and used this setup to simulate long-range Q projections. Meteorological datasets were sourced from gridded daily observations (1950-2013) and downscaled historical (1950-2005) and future projections (2006-2099) derived from multiple CMIP5 models under a low and a high emission scenario to explore forcing uncertainties and cases where P increase could offset warming. We compared the impacts of anticipated climate change on hydrologic responses in subbasins key for water management to gauge their importance for basin-wide water budgets and how these relationships could evolve in time, as this has been a largely unexplored aspect in the CRB. Results showed that spatial gradients in seasonal P changes led to contrasting seasonal responses in runoff (R) across the CRB. Whereas most of the Upper Basin had a shift to greater R during the winter, summer R declined over most of the CRB due to heightened evapotranspiration in the northwest (Green, Upper Colorado, Glen Canyon, and Grand Canyon subbasins) and large P decline in the southeast (San Juan, Little Colorado, and Gila subbasins). The strength of seasonal runoff signals across different climate models and their impacts to annual Q were dependent on subbasin area and emission scenario. Annual Q at the CRB outlet declined in most cases, however, reflecting the pervasive drying effect of warming.
How to cite:
Whitney, K., Vivoni, E., Bohn, T., Wang, Z., Xiao, M., and Mascaro, G.: Increased Temperatures Overwhelm Precipitation Changes Leading to Streamflow Declines in the Colorado River Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3151, https://doi.org/10.5194/egusphere-egu21-3151, 2021.
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Eugene Muzylev, Zoya Startseva, Elena Volkova, and Eugene Vasilenko
The method is developed to calculate soil water content W, evapotranspiration Ev and other water and heat regime (WHR) characteristics of agricultural regions for vegetation season (VS). The base of the method is the physical-mathematical model of vertical water and heat transfer in the “Soil-Vegetation-Atmosphere” system (SVAT), suitable for utilizing satellite-retrieved estimates of vegetation and meteorological characteristics such as vegetation index NDVI, emissivity E, vegetation cover fraction B, leaf area index LAI, precipitation, and land surface temperature LST. These estimates were built under thematic processing satellite data obtained by radiometers AVHRR/NOAA, SEVIRI/Meteosat-10, -11, -8; MSU-MR/Meteor-M No 2 in visible and IR ranges. Soil and vegetation characteristics were the model parameters and meteorological characteristics were considered to be the input variables.
The case study was carried out for forest-steppe territory of 227,300 km2 located in the Central Black Earth Region of European Russia, for steppe black earth Rostov region of 100,000 km2, and for arid steppe territory of the Saratov and Volgograd Trans-Volga region of 66,600 km2 for VS of 2017-2018.
Estimates of daily, ten-day and monthly precipitation sums were carried out using the Multi Threshold Method for detecting cloudiness, identifying its types, allocating precipitation zones and determining rainfall intensity maximum. The key point of the method is the transition from assessing the rainfall intensity to estimating its daily sums.Comparing calculated daily, ten-day and monthly rainfall sums with each other for all sensors and with similar ground-based data showed the coincidence of the satellite-detected and actual precipitation zones in 75-85% of cases for each radiometer.
Satellite LST estimates were retrieved by the Generalized Split-Window method using the regression equations for the satellite-measured radiation temperature. Comparison of these estimates with each other for all radiometers, with the model calculation results and with ground-measured air temperature values for named VS showed their differences to be within acceptable limits.
Because of the different climatic conditions in the study areas, the empirical formulae to calculate B and LAI were analyzed and their detailed estimates were made, the errors of which were about 15 and 20%, respectively.
The possibility to use soil surface moisture estimates obtained from the scatterometer ASCAT/MetOp data in the microwave range for modeling is shown (to select initial conditions when calculating W and to assess evaporation from soil surface).
To calculate W, Ev and other WHR components the developed procedures to assimilate satellite-retrieved B, LAI, precipitation and LST estimates in the model were adapted to the territories under study. These procedures included replacing ground-based estimates of these values by their satellite-retrieved estimates in all computational grid nodes at each time step. The efficiency of these procedures was confirmed by comparing modeled and measured values of W and Ev. The final modeling results are distributions of W, Ev and other WHR components over the areas of interest. Estimation errors for W (10-15%) and Ev (20-25%) (even for the arid Trans-Volga region) are acceptable values.
As a conclusion, the developed method can be used to assess water resource components for vast agricultural regions.
How to cite:
Muzylev, E., Startseva, Z., Volkova, E., and Vasilenko, E.: Utilization of satellite data on meteorological and land surface characteristics in the model of water and heat exchange for vast agricultural region territories, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6739, https://doi.org/10.5194/egusphere-egu21-6739, 2021.
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The Indus River Basin (the Indus) is facing the threat of great water shortages due to rapid population growth, expanding of irrigation area and increasing meltwater from snow and ice under the background of global warming. Being a less gauged basin, the effective usage of water resources in the Indus is always challenged by the high variability of the surface and ground water under a warming climate. This study therefore aimed to characterize and uncover the driving force of changes in water storage in the Indus based on GRACE and GRACE-FO solutions. A series of statistical techniques, such as EOF, modified STL, and Mann-Kenddall test, were applied to quantify and attribute the spatiotemporal patterns of the water storage dynamics. Our results demonstrated that (1) terrestrial water storage anomaly (TWSA) of the basin displayed a deficit and the deficit was largely concentrated in the middle and upper Indus plain (MUIP) during 2002 and 2020. (2) A slight decline in TWSA in the upper Indus basin (UIB) might be attributed to the accelerated melting of glacier and snow cover. (3) The excessive withdrawal of groundwater (1.57 mm/month) dominated the decrease of TWSA over the MUIP although weak increase of precipitation happened in the region. Anthropogenic activities imposed approximately 86.9% impact of the decrease in groundwater and this impact will aggravate for a long time if no effective water management schemes are taken. (4) Influenced by favorable meteorological conditions, the precipitation presented positive trend against the weakness of the India Summer Monsoon and the Westerlies, which exerted the positive influence on TWSA.
How to cite:
Zhu, Y., Liu, S., Yi, Y., Xie, F., and Miao, W.: Overview of terrestrial water storage changes over the Indus River Basin based on GRACE/GRACE-FO solutions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4172, https://doi.org/10.5194/egusphere-egu21-4172, 2021.
Roberta Perico, Paolo Frattini, Giovanni Battista Crosta, and Philip Brunner
Attaining a comprehensive and reliable water balance of snow-dominated alpine catchments is fundamental for a holistic representation of the hydrological and hydrogeological processes. In fact, their contribution to the water balance is extremely important for the water resources management and for a reliable estimation of groundwater recharge. A major limitation to the elaboration of these balances in alpine terrain are the difficultly of data acquisition as well as the limited presence of meteorological stations. These two factors considerably increase the uncertainty of water balances. Remotely sensed data can provide valuable information for the balance elaboration at a regional scale. Among the satellite data available, the Sentinel data, collected in the ESA missions in the last 6 years, has provided free and global access of observations including optical, thermal, and microwave sensors with high spatial and temporal resolutions.
In the present work, we estimated groundwater recharge (R) for the last two hydrologic years (from March 2018 to March 2020), based on satellite data. For this purpose, the most recent methods and databases based on satellite observations were tested: time series of the precipitation (P), the snow water equivalent (SWE), and the evapotranspiration (ET) were retrieved in an extensive Alpine catchment (26,000 km2) located in northern Italy. Daily precipitation was calculated from PERSIANN-Cloud Classification System (PERSIANN-CCS, Hong et al. 2004) database at the resolution of 4.0 km. ET was estimated with the combined use of Sentinel 2 and 3 satellites (Guzinski et al., 2020) at a resolution of 20 m and with weekly return period. The weekly SWE was calculated starting from Sentienl 1 (C-SNOW database, Lievens et al., 2019) and Sentienl 2, at the spatial resolution of 30 m.
Based on available measurements of P, ET, and snow depth in the catchment, the uncertainty of the hydrologic estimations was quantified. We further carried out a sensitivity analysis, considering the physiographic parameters (altitude, slope, and aspect) and the seasonal conditions. For SWE estimates, an altitude-dependent effect and a lower accuracy in the snowmelt phase have been observed. The results show that the adopted satellite-based methods allow obtaining consistent and physically realistic values of recharge, with relatively low uncertainty.
References:
Guzinski, R., Nieto, H., Sandholt, I., & Karamitilios, G. (2020). Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion. Remote Sensing, 12(9), 1433.
Hong, Y., Hsu, K. L., Sorooshian, S., & Gao, X. (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology, 43(12), 1834-1853.
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., ... & Jonas, T. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature communications, 10(1), 1-12.
How to cite:
Perico, R., Frattini, P., Crosta, G. B., and Brunner, P.: Establishing a water balance of an Alpine catchment using satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2130, https://doi.org/10.5194/egusphere-egu21-2130, 2021.
Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm2. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m2 and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it’s inherently difficult to estimate UVB only based on canopy openness result.
How to cite:
Zhang, Y., Onda, Y., Kato, H., Sun, X., and Gomi, T.: Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14670, https://doi.org/10.5194/egusphere-egu21-14670, 2021.
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