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Climate change, one of the major environmental challenges facing mankind, has caused intermittent droughts in many regions resulting in reduced water resources. This study investigated the impact of climate change on the characteristics (occurrence, duration, and severity) of meteorological drought across Ankara, Turkey. To this end, the observed monthly rainfall series from five meteorology stations scattered across Ankara Province as well as dynamically downscaled outputs of three global climate models that run under RCP 4.5 and RCP 8.5 scenarios was used to attain the well-known SPI series during the reference period of 1986–2018 and the future period of 2018–2050, respectively. Analyzing drought features in two time periods generally indicated the higher probability of occurrence of drought in the future period. The results showed that the duration of mild droughts may increase, and extreme droughts will occur with longer durations and larger severities. Moreover, joint return period analysis through different copula functions revealed that the return period of mild droughts will remain the same in the near future, while it declines by 12% over extreme droughts in the near future.

International audience; Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions. View Full-Text

Soil moisture, evapotranspiration, and agricultural drought are so closely interlinked phenomena that deficiency in soil moisture causes reduction in ET, which eventually leads to vegetative stress and agricultural drought. Monitoring of soil moisture, ET, and vegetation condition is indispensable to identify terrestrial drought conditions to take proactive measures and mitigate its negative consequences. Although conventional methods such as the point-based measurements of soil moisture, evapotranspiration, precipitation, and temperature records measured at the ground stations (e.g., soil moisture networks, meteorological stations, and Eddy covariance flux towers) provide accurate discreet observations, they are unable to accurately capture the conditions in places between the ground stations. Therefore, the techniques based on remote sensing inputs are important to track the spatially continuous estimates of soil moisture, ET, and vegetation condition from the regional to global scales, while the point-based estimates of soil moisture, ET, precipitation, and temperature can be utilized to assist in validating satellite-based estimates of soil moisture, ET, and agricultural drought. In this chapter, three distinct proven methods to estimate soil moisture, ET, and agricultural drought using satellite and ancillary data will be introduced, and then, some example maps and their validation results against the ground truth will be presented.

This study evaluates relative performances of different statistical algorithms to enhance radar-based quantitative precipitation estimation (QPE) accuracy using rain gauge network data. Initial investigations are implemented using observations obtained via 17 C-band radars located over different regions of Turkey. It was observed that there is an underestimation problem in radar estimations compared with the ground observations. According to the initial results, daily mean bias for radar estimations over different precipitation events is about -1.4 mm/day in average. Implemented statistical methodologies include Mean Field Bias (MFB), Local Multiplicative Bias (LMB), Local Additive Bias (LAB), Local Mixed Bias (LMIB), Multiple Linear Regression (MLR) adjustment, and Cumulative Distribution Function (CDF) Matching techniques. To test the performance of these algorithms, cross-validation methods have been used. In cross-validation, 50%, 25%, 12.5% of the station-based observations are excluded for validation while the remaining are used for the calibration in different experiments. Both the calibration and validation results obtained from all rainfall events of 2017 suggest that LMB and LAB adjustment methods perform better both in terms of compensating the underestimation and decreasing the RMSE values. Primary results show that methods mentioned help reduce the underestimation problem by increasing the daily mean error from -1.4 mm up to -0.4 mm and decreasing the daily RMSE values from 6.2 mm/day to 0.80 mm/day in rainy days in average. Despite the fact that proposed time-independent MLR and CDF methods are shown to be compensating a large portion of radar precipitation underestimation (according to the initial results, from -1.4 mm/day into -0.5 mm/day in average), estimations obtained from these methods have higher uncertainties in estimating the precipitation amount especially in areas with higher probability of convective precipitation type (no significant increment in RMSE values). By utilizing the best methods among all bias adjustment methods, a high-resolution composite radar-based precipitation map of Turkey is currently being produced. For validating the final product, three independent networks of collocated rain-gauges will be used. Similar results are being expected from the final validation process. Nevertheless, the outputs of this validation process will help understand the relative performance of the bias correction algorithms in the areas with overlapping radar estimations. Keywords — Merging, radar precipitation estimation, gauge adjustment

Monitoring agricultural crop conditions during the growing season and estimating potential crop yields are important for evaluating seasonal production. The accurate and timely assessment of the losses in crop yields caused by a natural disaster, such as drought, may be critical for countries where their economies are reliant on their agricultural productivity. Early assessment of the reduction in crop yields can prevent a catastrophic situation and help meet the demands of strategic planning.In this study, the Multiple Linear Regression model was used to estimate the wheat yields in Turkey. Remotely sensed-, model-, and in-situ-based measurements of affecting variables of crop productivity (i.e., precipitation, land surface temperature, soil moisture, wind, and Normalized Vegetation Difference Index) were extracted over selected areas in which yield data were available on them. The datasets are collected using different time scales (e.g., before/during sowing period, growing season, one/two months before harvest, etc.).The cross-validation of more than 700 different model combinations over more than total 700 different administrative divisions (i.e., districts, provinces, and regions) showed that by using the best model selected for each district, on average, a correlation value of 0.65 and a mean absolute error of 35 kg/da can be obtained between estimated and observed yield values. While, this consistency is more pronounced over the districts located in the Central Anatolia region where the average production of the wheat in them is more than the rest of districts in the country. Overall, regional differences of the selected predictors of observed yield data, suggest that the land surface temperature can provide a useful exploratory and predictive tool for wheat yield estimation across the country.

Global numerical weather prediction models (NWP) such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Forecast System (GFS) generate atmospheric data for the entire world. However, these models provide the data at large spatiotemporal resolutions because of computational limitations. Weather Research and Forecasting (WRF) Model is one of the models, which is capable of dynamically downscaling the NWP models’ output. In this study, all combinations of 4 microphysics and 3 cumulus parametrization schemes, 2 planetary boundary layers (PBL), 2 initial and lateral boundary conditions and 2 horizontal grid spacing (i.e., an ensemble consisting of 96 different scenarios) are simulated to measure the sensitivity of WRF-derived precipitation against different model configurations. The sensitivity analyses are performed for 4 separate events. These events are selected among the extreme precipitation events in the Mediterranean (MED) and eastern Black Sea (EBLS) regions. For each region, a summer and an autumn event are chosen. Here, the fundamental aim is to determine the spatiotemporal differences in WRF input parameters that yield better outcomes. A total of 72 hours simulations are started 24 hours before the event day to avoid spin-up time error. The model is adjusted to produce hourly precipitation outputs. The relative performance of scenarios is measured using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method considering 5 categorical validation indices and 4 pairwise statistics calculated between the model estimations and the ground-based precipitation observations. According to the TOPSIS results, microphysics scheme, initial and lateral boundary condition, and horizontal grid spacing are substantially influential on WRF precipitation estimates, while cumulus parameterization has a comparatively low effect. The choice of PBL scheme is essential for the summer events, but the results of the autumn events are independent of PBL selection. WRF products are better for the events of the EBLS basin when ERA5 is used as the initial and lateral boundary condition. On the contrary, GFS is superior in the MED region. In terms of spatial resolution, 9 km horizontal grid spacing is commonly preferable for all the events rather than 3 km. Besides, the model underestimates the area-averaged precipitation amounts except for the MED-autumn incident. Still, the model is successful at catching the peak hours of all events. Moreover, the precipitation detection ability of WRF is better for the autumn months. The probability of detection index is higher than 0.5 at 35% of MED stations and 68% of EBLS stations for the autumn events. The local and convective summer events are investigated considering the event centers. Albeit relatively low relationships are defined for the MED-summer event, a statistically significant correlation is obtained between the central station of the EBLS-summer event and the closest grid for the predictions of 52 scenarios (i.e., 54% of the ensemble).

This study investigates the impact of sea surface temperature (SST) describing the lower boundary of Weather Research and Forecasting (WRF) model on the extreme weather events that occurred on the Mediterranean (MED) and the Eastern Black Sea (EBS) regions of Turkey. For each region, one extreme event case is selected and characterized as a summer convective system in EBS and as a winter synoptic system in MED region. The SST impact on the WRF model forecasts of these events is accomplished in two ways. First, the SST analysis is conducted by making a 10-days simulation for each event with and without activating the SST options (update and skin options) available in the model configuration. In these runs, the selected initial and boundary condition datasets, Global Forecasting System (GFS) for the MED region and the ERA5 for the EBS regions, provide corresponding internal SST data. Second, the prediction performance of the model is evaluated among the simulations of non-external, time-varying (GFS and ERA5 SST) and external, time–varying, high-resolution SST products (The Group for High Resolution Sea Surface Temperature; GHRSST, Medspiration, and NCEP) for each event. The results show that the WRF model simulations, even for short-term event predictions, are highly sensitive to time-variant SST options. The overestimation and high spreading feature of maximum and total precipitation are realistically reduced by time-variant SST products. The Medspiration and the NCEP sources for the MED region and the GHRSST and the Medspiration sources for the EBS region yielded warmer SSTs among the simulations. By establishing the amplified air-sea interactions, these sources provide more appropriate peak precipitation distributions in both regions. Furthermore, based on this study, the effect of using high-resolution SST (GHRSST and Medspiration) is more prominent in predicting convective event developed over complex topography of the EBS while the resolution effect is not much critical for synoptic system simulation even under the complex topography in MED. Lastly, the highest mean correlation of conditional hourly rain is calculated as 0.65 with the Medspiration product on MED and 0.48 with the GHRSST product on the EBS region.

To effectively meet growing food demands, the global agronomic community will require a better understanding of factors that are currently limiting crop yields and where production can be viably expanded with minimal environmental consequences. Remote sensing can inform these analyses, providing valuable spatiotemporal information about yield-limiting moisture conditions and crop response under current climate conditions. In this paper we study correlations for the period 2003–2013 between yield estimates for major crops grown in Brazil and the Evaporative Stress Index (ESI) – an indicator of agricultural drought that describes anomalies in the actual/reference evapotranspiration (ET) ratio, retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The strength and timing of peak ESI-yield correlations are compared with results using remotely sensed anomalies in water supply (rainfall from the Tropical Rainfall Mapping Mission; TRMM) and biomass accumulation (LAI from the Moderate Resolution Imaging Spectroradiometer; MODIS). Correlation patterns were generally similar between all indices, both spatially and temporally, with the strongest correlations found in the south and northeast where severe flash droughts have occurred over the past decade, and where yield variability was the highest. Peak correlations tended to occur during sensitive crop growth stages. At the state scale, the ESI provided higher yield correlations for most crops and regions in comparison with TRMM and LAI anomalies. Using finer scale yield estimates reported at the municipality level, ESI correlations with soybean yields peaked higher and earlier by 10 to 25 days in comparison to TRMM and LAI, respectively. In most states, TRMM peak correlations were marginally higher on average with municipality-level annual corn yield estimates, although these estimates do not distinguish between primary and late season harvests. A notable exception occurred in the northeastern state of Bahia, where the ESI better captured effects of rapid cycling of moisture conditions on corn yields during a series of flash drought events. The results demonstrate that for monitoring agricultural drought in Brazil, value is added by combining LAI with LST indicators within a physically based model of crop water use.

The performances of a large number of Global Circulation Models (GCMs) belonging to the latest release the Coupled Model Intercomparison Project phase 6 (CMIP6) in comparison with its predecessor CMIP5 are evaluated for monthly precipitation and temperature over Turkey (i.e. climate change hotspot). The ensemble of best top four models from both CMIP simulations is also used to investigate the future climate change under medium and high emissions for short, medium, and long terms. Results show CMIP6 products outperform CMIP5 in terms of accuracy statistics, particularly for precipitation. Overall, CMIP6 indicate less intermodel variability, implying the climate signal obtained via CMIP6 is improved relative to CMIP5. The CMIP6 reduce the error in precipitation and temperature 11% and 6%, respectively while increasing the correlation 13% for precipitation. Correlation is not changed for temperature. The substantial success of CMIP6 mainly manifests itself in the winter precipitation and autumn temperature. A slightly warmer (up to 0.35 °C) climate from the CMIP6 is projected while precipitation change in mean precipitation decline is about 2.5% smaller for CMIP6. However, precipitation changes in maximum decline and increase are more noticeable for CMIP6, addressing the better resolving for future climate variability. The summer warming (up to 6.5 °C) and drying (up to −40%) towards autumn spread across the country. The increased precipitation projection in the winter and spring over the east of the Black Sea Region and north-east of Anatolia may further increase the flood risks in the area.

The annual flood cycle of the Sudd wetland in South Sudan plays an important role in the Nile River Basin water balance. The wetland, however, is extensive and sparsely instrumented, which has inhibited credible understanding of regional flooding across space and time. Here we explore the potential to apply low resolution C-band ENVISAT Advanced SAR imagery for remote estimation of Sudd flooded area. Over a five year study period (2007–2011) the time-averaged flooded Sudd area was found to be 18,033 km 2 with an average annual high of 29,702 km 2 in late September and a low of 10,128 km 2 in early May. Annual peak flood area ranges considerably from 19,259 km 2 in 2009 to 36,649 km 2 in 2007, but we found no systematic trend over the five year study period. Derived flood frequency maps identify areas of open water and permanent flooding (12% of total area), seasonal flooding (29%), and intermittent flooding (48%). To evaluate the certainty of our results, we consider their consistency with (1) prior studies, (2) evapotranspiration estimates from the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, (3) watershed storage anomaly estimates from GRACE, (4) supervised classification of open water area using Landsat, and (5) a rough measure of water availability (antecedent precipitation). The analyses show reasonable temporal and spatial consistency with available lines of evidence. We conclude that low resolution C-band SAR imagery shows promise for study of Sudd wetland flood dynamics.

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

The physics- and initial-based ensemble approach for Weather Research and Forecasting (WRF) model is applied for predicting four different extreme precipitation events that occurred in summer and autumn over the two most flood-prone regions of Turkey, namely; the Eastern Black Sea (EBS) and Mediterranean (MED). A total of 48 runs, each of which includes two nested domains, is designed considering four microphysics (MP), three cumulus (CU), two planetary boundary layer (PBL) schemes, and two initial forcing datasets (ERA5 and Global Forecast System (GFS)) for each event. The forecast skill of the runs is measured through the hierarchic application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method using five categorical and four statistical metrics. The best ten members for each event are validated on the independent events. The MP scheme is the most influential on precipitation estimates between the schemes, especially for the autumn events. However, the choice of a specific MP scheme varies with season and region. Similarly, PBL and CU schemes show a high dependency on event timing and location. Forecasts based on ERA5 yield the highest accuracy over EBS, while GFS-based forecasts are better in the MED region. Most of the top ten members are represented by runs from the 9-km domain. The model parameterization generally shows a more significant impact than model initiation on precipitation variability through spatiotemporal runs. Also, the dynamic downscaling of ERA5 through the WRF model indeed produces better precipitation estimates than ERA5 products over topographically complex regions.

Accuracy assessment of precipitation retrievals is a pre-requisite for many hydrological studies as it helps to understand the source and the magnitude of the uncertainty in hydrological response variables, particularly over regions with complex topography. This study evaluates GPM IMERGv05, TMPA 3B42V7, ERA-Interim, and ERA5 precipitation products using 256 ground-based gauge stations between 2014 and 2018 over Turkey known to have complex topography and varying climate. Error statistics, categorical performance indices, and intensity-frequency distribution of the precipitation products are investigated over varying wetness and terrain slope classes. Results show that while the monthly correlation coefficient averaged over all the products is 0.79 for regions having slopes between 5 and 10%, this value drops to 0.74 when the slope is above 15%. IMERG, TMPA and ERA-Interim products underestimate the observed precipitation over relatively wetter regions, while they overestimate over relatively drier regions and relatively higher slopes. ERA5 consistently overestimates the observed precipitation over all the wetness and slope classes. Overall, TMPA shows the smallest wet bias (0.1 mm/day), while ERA5 has the largest wet bias (0.5 mm/day). IMERG has the highest monthly correlation (i.e., 0.82 when averaged for all 256 stations) and ERA-Interim has the lowest correlation (0.77) against the ground-based observations. The successors (IMERG and ERA5) show smaller ErrSD and higher CC compared to their predecessors (TMPA and ERA-Interim) at both daily and monthly time scales. The results also show that the studied satellite-based products perform better in matching the frequency of daily precipitation intensities, whereas the studied model-based products perform better in terms of categorical performance indices for lighter precipitation events.

Summary Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003–2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.

In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere–Land Exchange Inverse (ALEXI) Source–Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source–sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source–sink processes within modern LSMs.

Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has specifically investigated the degree to which errors in existing soil moisture datasets conform to these assumptions. Here these assumptions are evaluated both analytically and numerically over four extensively instrumented watershed sites using soil moisture products derived from active microwave remote sensing, passive microwave remote sensing, and a land surface model. Results demonstrate that nonorthogonal and error cross-covariance terms represent a significant fraction of the total variance of these products. However, the overall impact of error cross correlation on TCA is found to be significantly larger than the impact of nonorthogonal errors. Because of the impact of cross-correlated errors, TCA error estimates generally underestimate the true random error of soil moisture products.

[1] Land data assimilation systems are commonly tasked with merging remotely sensed surface soil moisture retrievals with information derived from a soil water balance model driven by observed rainfall. The performance of such systems can be degraded by the incorrect specification of parameters describing modeling and observation errors. Here the Auto-Tuned Land Data Assimilation System (ATLAS) is introduced to simultaneously solve for all parameters required for the application of a simple land data assimilation system to integrate satellite-based rainfall and soil moisture retrievals for drought monitoring applications. The approach is based on combining a triple collocation (TC) strategy with the statistical analysis of filtering innovations and designed to leverage the simultaneous availability of satellite-based soil moisture products acquired from both active and passive microwave remote sensing. A number of variants of the ATLAS approach—each based on a different strategy for leveraging TC and innovation analysis within an adaptive filtering framework—are derived and evaluated through a synthetic twin experiment. In addition, a preliminary real data analysis is conducted using actual satellite-based products and evaluated against independent ground-based observations. Results illustrate the potential of ATLAS to improve the analysis of soil moisture anomalies using data products derived from the Global Precipitation Measurement (GPM) and the NASA Soil Moisture Active/Passive missions.

It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocation. Here, the authors evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation–based rescaling method results in an optimal solution, whereas variance matching and linear least squares regression approaches result in only approximations to this optimal solution.

[1] Land surface temperature plays an important role in surface processes and is a key input for physically based retrieval algorithms of soil moisture and evaporation. This study presents a framework for using independent estimates of land surface temperature from five microwave satellite sensors to improve the accuracy of land surface temperature output from a numerical weather prediction system in an off-line (postprocessing) analysis. First, structural differences in timing and amplitude of the temperature signal were addressed. Then, satellite observations were assimilated into an auto-regressive error model, formulated to estimate errors in the numerical weather prediction output. Errors in daily minimum and amplitude were treated separately. Results of this study provide new insights about potential added benefits of preprocessing and off-line assimilation of microwave remote sensing-based and model-based temperature retrievals. It is shown that the satellite observations may be used to reduce errors in surface temperature, particularly for day-time hours. Preprocessing is responsible for the bulk of this reduction in temperature error; data assimilation is shown to further reduce the random temperature error by a few tenths of a Kelvin, accounting for a 10% reduction in RMSE.

It is well known that the ensemble Kalman filter (EnKF) requires updating each ensemble member with perturbed observations in order to produce the proper analysis-error covariances. While increased accuracy in a mean square sense may be preferable in many applications, less accuracy might be preferable in other applications, especially if the variables being assimilated obey certain conservation laws. In land data assimilation, for instance, the update in soil moisture often produces a water balance residual, in the sense that the input water is not equal to output water. This study shows that suppressing the perturbation of observations in the EnKF and in the weakly constrained ensemble Kalman filter significantly improves the water balance residuals without significantly increasing the state errors.

A weak constraint is introduced in ensemble Kalman filters to reduce the water budget imbalance that occurs in land data assimilation. Two versions of the weakly constrained filter, called the weakly constrained ensemble Kalman filter (WCEnKF) and the weakly constrained ensemble transform Kalman filter (WCETKF), are proposed. The strength of the weak constraint is adaptive in the sense that it depends on the statistical characteristics of the forecast ensemble. The resulting filters are applied to assimilate synthetic observations generated by the Noah land surface model over the Red Arkansas River basin. The data assimilation experiments demonstrate that, for all tested scenarios, the constrained filters produce analyses with nearly the same accuracy as unconstrained filters, but with much smaller water balance residuals than unconstrained filters.

Retrieval of soil moisture content using the vertical and horizontal polarizations of multiple frequency bands on microwave sensors can provide an estimate of vegetation water content (VWC). Another approach is to use foliar-water indices based on the absorption at shortwave-infrared wavelengths by liquid water in the leaves to determine canopy water content, which is then related to VWC. An example of these indices is the normalized difference infrared index (NDII), which was found to be linearly related to canopy water content using various datasets, including data from the Soil Moisture Experiments 2002 and 2005 in central Iowa. Here we compared independent estimates of VWC from WindSat to Moderate resolution Imaging Spectroradiometer (MODIS) NDII over central Iowa from 2003 to 2005. Results showed that there was a linear relationship between the MODIS and WindSat estimates of VWC, although WindSat-retrieved VWC was greater than MODIS-retrieved VWC. WindSat and MODIS have different satellite overpass times and in most climates we expect VWC to vary over a day due to transpiration and plant water stress. However, a sensitivity analysis indicated that the diurnal variation of VWC should not have a significant effect on retrievals of VWC by either method. The results of this study indicated that soil moisture retrievals from microwave sensors may be improved using VWC from optical sensors determined by foliar-water indices and classifications of land cover type.

This paper tests whether seasonal mean precipitation is predictable using a new method that estimates and analyzes joint probabilities. The new estimation method is to partition the globe into boxes, pool all data within the box to estimate a single joint probability of precipitation for two consecutive seasons, and then apply the resulting joint probability to individual pixels in the box. Pooling data in this way allows joint probabilities to be estimated in relatively small sample sizes; however, the new method assumes that the transition probabilities of pixels in a box are homogeneous and stationary. Joint probabilities are estimated from the Global Precipitation Climatology Project dataset in 21 land boxes and 5 ocean boxes during the period 1979–2008. The state of precipitation is specified by dry, wet, or normal terciles of the local climatological distribution. Predictability is quantified by mutual information, which is a fundamental measure of predictability that allows for nonlinear dependencies, and is tested using bootstrap methods. Predictability was verified by constructing probabilistic and quantitative forecasts directly from the transition probabilities and showing that they have superior cross-validated skills than forecasts based on climatology, persistence, or random selection. Spring was found to be the most predictable season, whereas summer was the least predictable season. Analysis of joint probabilities reveals that although the probabilities are close to climatology, the predictability of precipitation arises from a slight tendency of the state to persist from one season to the next, or if a transition occurs, then it is more often from one extreme to normal than from one extreme to the other.

This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R2 of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R2 of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76–0.90 μm wavelength) and band 5 (1.55–1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R 2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.

Bu çalışmada, TropicalRainfall Measuring Mission (TRMM, Tropik Yağmur Ölçme Misyonu) 3B42 v7 uydukaynaklı aylık yağmur verileri, Meteoroloji Genel Müdürlüğü tarafından işletilen257 adet gözlem istasyonunda 1998 - 2014 yılları arasında ölçülen yağışverilerinin aylık kümülatif toplamları kullanılarak doğrulanmıştır. İstasyon veuydu kaynaklı yağış verilerinin uzun dönem ortalama değerleri, birbirleriarasındaki korelasyonları, aylık ortalama ve anomali bileşenlerinin standartsapmaları ile uydu verilerinin hata oranlarının standart sapmaları incelenmiştir.Uydu verilerinin hata oranlarının yüksekliğe ve kıyılara olan uzaklığa bağlıdeğişimi çalışmanın odak noktası olmuştur. Bulgular, özellikle uydu kaynaklı yağmurverilerinde anomali bileşeni hata oranlarının düşük kotlu bölgeler ve kıyıkesimleri için yüksek kotlu bölgeler ve iç kesimlere kıyasla çok daha yüksekolduğunu ortaya koymaktadır.

Retrieval of soil moisture content from microwave sensors also returns an estimate of vegetation water content. Remotely sensed indices from optical sensors can be used to estimate canopy water content. For corn and soybean in central Iowa, there are allometric relationships between canopy water content and vegetation water content. The Normalized Difference Infrared Index from MODIS was used to estimate vegetation water content. We compared independent estimates of vegetation water content from WindSat and MODIS over central Iowa from 2003 to 2005. There was a strong linear relationship between the MODIS and WindSat estimates, but the WindSat estimates were about two times higher. These results suggest that soil moisture retrievals from microwave sensors may be more accurate with estimates of vegetation water content from optical sensors.