Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

Long-term forest monitoring unravels constant mortality rise in European forests

European forests are an important source for timber production, human welfare, income, protection and biodiversity. During the last two decades, Europe has experienced a number of droughts which were exceptionally within the last 500 years both in terms of duration and intensity and these droughts seem to left remarkable imprints in the mortality dynamics of European forests. However, systematic observations on tree decline with emphasis on single species together with high-resolution drought data has been scarce so far so that deeper insights into mortality dynamics and drought occurrence is still limiting our understanding at continental scale. Here we make use of the ICP Forest crown defoliation dataset, permitting us to retrospectively monitor tree mortality for four major conifers, two major broadleaves as well as a pooled dataset of nearly all minor tree species in Europe. In total, we analysed more than 3 million observations gathered during the last 25 years and employed a high-resolution drought index which is able to assess soil moisture anomaly based on a hydrological water-balance and runoff model every ten days globally. We found significant overall and species-specific increasing trends in mortality rates accompanied by decreasing soil moisture. A generalized linear model identified previous-year soil moisture anomaly as the most important driver of mortality patterns in European forests. Significant interactions appeared between previous-year soil moisture and stand water regime in conifers, strongly suggesting that conifers growing at productive sites are more vulnerable under drought. We conclude that mortality patterns in European forests are currently reaching a concerning upward trend which could be further accelerated by global change-type droughts.

Drought occurrence in key regions of soil moisture-atmosphere interaction in temperate Eurasia

<p>In an atmospheric general circulation drought-forming anomaly the nonlinear relationship between soil moisture and evapotranspiration play an important role in transitional (sub-humid and semi-dry) moisture regime. In this study the preceding soil moisture deficit was linked to the following low standardized precipitation index (SPI) indicating atmospheric drought in two major land-atmosphere coupling regions over Eurasia – Northern Eurasian Plains (NEP) and Plains and Uplands of Northeastern China (PUNEC).  Spring season was under consideration as the most significant for crop development failure due to lack of moisture and the most predictable due to prolonged soil memory after major hydrological event of the year – the snowmelt. The Global Energy and Water Exchanges (GEWEX) project deliverables and Climate Prediction Center (CPC) soil moisture data were used after validation with agrometeorological station data. It was shown that May droughts in NEP and PUNEC occur after regional negative soil moisture anomaly in early spring in significantly high proportion of cases for the study period 1985-2019. The soil moisture anomaly is leading to drought when the specific circulation pattern is formed as shown by the composite analysis. Importantly, the circulation pattern is Eurasia-broad with upstream blocking ridge centered in NEP and anticyclone formation in PUNEC. Both ridge and anticyclone are persistent and characterized by low cloudiness, reduced moist static energy (also due to reduction in evapotranspiration by low soil moisture) and low large scale and convective precipitation. That is why low SPI events often co-occur in two study regions. Atmospheric models tend to agree that atmospheric processes do respond to negative anomalies in surface moisture conditions in NEP and PUNEC and positive feedback of soil drought on the atmosphere is largely responsible for enabling atmospheric aridity extremes. The reasons for the simultaneous early spring moisture deficits in two regions are to be searched in the features of winter general circulation which lead to reduced snow accumulation and/or snowmelt regime with lower than average water infiltration to the soil. European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble seasonal forecast skill was also explored. SPI skill scores in April are indicating better forecast in NEP than in PUNEC but skill decreases sharply in May in NEP while remaining high till June in PUNEC. Further prospects for improving meteorological, hydrological and agricultural drought forecasting and forecast post-processing methodology for the regions of study are discussed.</p><p>This study was supported by the Russian Federal Targeted Program 1.2. Grant Number RFMEFI60419X0222 “Global climate and agrolandscapes of Russia: development of assessment and risk management system of Russian chernozems degradation” and National Key Research and Development Program of China, Grant Number 2016YFA0600701 “The variation and mechanism of extreme climate in northern China at interannual timescale”</p>

On the Spatial Gradient of Soil Moisture–Precipitation Feedback Strength in the April 2011 Drought in the Southern Great Plains

The southern Great Plains (SGP) experienced a record-breaking drought in 2011, in which the excessively dry conditions established quickly in spring (i.e., April) and extended into summer. A regional climate model is used (after its evaluation) to simulate this April drought and investigate how a soil moisture anomaly could affect the development of its precipitation deficit. The authors examine how the local thermodynamic structure of the overlying atmosphere contributes to soil moisture feedbacks and how these feedbacks are connected to nonlocal mechanisms. The simulations establish a zonal gradient in the (generally positive) feedback strength [i.e., a significant (negligible) precipitation increase over the eastern (western) SGP] under an SGP-wide wet soil moisture anomaly and spatially similar evapotranspiration (ET) increments. This pattern is dominated by convective precipitation and consistent with spatial gradients in parameters relevant to moist convection, including the precipitable water, the low-level instability and humidity, and the local cloud water content. All these variables are sensitive to a wet soil moisture anomaly, but precipitation responds differently to their changes in different locations. Furthermore, the impacts of the soil moisture anomaly on various large-scale atmospheric fields are related to the spatial structure of feedback strength. Additionally, the weaker feedback over the western SGP occurs in a region of relatively strong subsidence and changes little with a westward expansion of the anomaly area, whereas nonlocal soil moisture impacts—in particular, moisture advection from the west—are important for the stronger feedback over the eastern SGP.

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). <br><br> The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October&ndash;April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct&ndash;Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

How Are Spring Snow Conditions in Central Canada Related to Early Warm-Season Precipitation?

The response of the warm-season atmosphere to antecedent snow anomalies has long been an area of study. This paper explores how the spring snow depth relates to subsequent precipitation in central Canada using ground observations, reanalysis datasets, and offline land surface model estimates. After removal of low-frequency ocean influences, April snow depth is found to correlate negatively with early warm-season (May–June) precipitation across a large portion of the study area. A chain of mechanisms is hypothesized to account for this observed negative relation: 1) a snow depth anomaly leads to a soil moisture anomaly, 2) the subsequent soil moisture anomaly affects ground turbulent fluxes, and 3) the atmospheric vertical structure allows dry soil to promote local convection. A detailed analysis supports this chain of mechanisms for those portions of the domain manifesting a statistically significant negative snow–precipitation correlation. For a portion of the study area, large-scale atmospheric circulation patterns also affect the early warm-season rainfall, indicating that the snow–precipitation feedback may depend on large-scale atmospheric dynamical features. This analysis suggests that spring snow conditions can contribute to warm-season precipitation predictability on a subseasonal to seasonal scale, but that the strength of such predictability varies geographically as it depends on the interplay of hydroclimatological conditions across multiple spatial scales.

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.