Proxy For Soil Moisture Research Articles

A New Wetness Index to Evaluate the Soil Water Availability Influence on Gross Primary Production of European Forests

Rising temperature, drought and more-frequent extreme climatic events have been predicted for the next decades in many regions around the globe. In this framework, soil water availability plays a pivotal role in affecting vegetation productivity, especially in arid or semi-arid environments. However, direct measurements of soil moisture are scarce, and modeling estimations are still subject to biases. Further investigation on the effect of soil moisture on plant productivity is required. This study aims at analyzing spatio-temporal variations of a modified temperature vegetation wetness index (mTVWI), a proxy of soil moisture, and evaluating its effect on gross primary production (GPP) in forests. The study was carried out in Europe on 19 representative tree species during the 2000–2010 time period. Results outline a north–south gradient of mTVWI with minimum values (low soil water availability) in Southern Europe and maximum values (high soil water availability) in Northeastern Europe. A low soil water availability negatively affected GPP from 20 to 80%, as a function of site location, tree species, and weather conditions. Such a wetness index improves our understanding of water stress impacts, which is crucial for predicting the response of forest carbon cycling to drought and aridity.

Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau

The Soil Moisture Active Passive (SMAP) satellite can no longer directly deliver high-resolution (9 km) soil moisture products with the failure of the onboard L-band radar. Thus, an appropriate replacement sensor and new algorithms are urgently needed to compensate for the lost radar and estimate soil moisture at high-resolution. This paper presents a new downscaling approach, the Downscaling basEd oN gradient boosting deciSion trEe (DENSE) method to solve this problem. We analyzed 26 soil moisture related indices, derived from MODIS and a digital elevation model, to identify proxy variables for soil moisture variability. A gradient boosting decision tree regression links the aggregated soil moisture proxies and SMAP observations at a coarse scale to express the nonlinear relationships between them. High-resolution soil moisture products were generated by applying this built regression model to the optimal soil moisture proxies at a fine scale over the entire Tibetan Plateau during the years 2015–2017. In situ measurements were collected from the Ngari, Naqu, and Maqu networks, which represent different climatic and vegetation conditions. We evaluated the downscaled soil moisture against ground observations at the daily, point, and network scales. The results indicated that the DENSE method effectively infers the spatio-temporal variability of soil moisture; and at the same time, preserves SMAP soil moisture accuracy. The performance of the proposed method failed in the Maqu network as this area is covered by dense vegetation. Although further improvements are still needed to correct vegetation effects, nevertheless, the DENSE method improved the spatial resolution of SMAP soil moisture estimates, from 36 km to 1 km over the Tibetan plateau where high-resolution soil moisture products are necessary to support local and global hydrological applications.

ADVANCES IN SOIL MOISTURE RETRIEVAL FROM NEAR-SURFACE MEASUREMENTS USING SATELLITE REMOTE SENSING

Abstract. Soil moisture influences numerous environmental processes occurring over large spatial and temporal scales. It profoundly influences the hydrological and meteorological activity together with climate predictions and hazard analysis. Space-borne sensors are capable of retrieving the surface soil moisture over a region on a regular basis. Latent heat measurements of soil, reflectance based methods, microwave measurements and synergistic approaches are some of the techniques used since long for providing soil moisture estimates over regional and global scales. Due to the dynamic interaction of soil with crops, retrieval of surface soil moisture is always challenging. This paper gives a brief overview of advance in soil moisture retrieval techniques, and an attempt to generate surface soil moisture from fine-resolution satellite remote sensing data. The optical remote sensing explores the linear relationship between land surface reflectance and soil moisture content, and through development of empirical spectral vegetation indices. Another way to estimate soil moisture emerged by measuring amplitude of diurnal temperature, which is closely related to thermal conductivity and heat capacity of soil. Emergence of radiometric satellite measurements at fine resolution has reached at a higher level of technology these days. Microwave remote sensing techniques have a long legacy of providing surface soil moisture estimates with reasonable accuracy. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Passive and Active) missions launched in 2009 and 2015 respectively, are completely dedicated for providing soil moisture at global scale with a spatial resolution of 35 km & 3–40 km. These soil moisture products, however, provides data at highly coarser spatial resolution. The launch of Sentinels gave insight by providing active radar and optical data at higher resolution (∼10 m). Sentinel-1 is the first SAR (Synthetic Aperture Radar) constellation having 6-day revisit time providing data in C-band with dual polarisations. However, no algorithm or methodology is available to generate surface soil moisture product at a finer resolution from dual polarisations. Sentinel-1 data has been used to generate regional surface soil moisture image through modelling. The same has been also used for generating surface soil moisture map of IARI farm at New Delhi. Dubois, a bare surface model, was tested for its suitability for surface soil moisture retrieval of the farm. In addition, radar- based Soil moisture (SM) proxy method was used over Sentinel-1 data for the month of July 2018, and validated through actual surface soil moisture (gravimetric) measurements. Results were satisfactory for a range of 4–16 m3 m−3 of soil moisture, with coefficient of determination (R2) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21–33 m3 m−3), which occurred after the rainfall, the R2 value reduced to 0.22 with larger RMSE. Results suggested that SM-proxy approach might work well for a limited range (drier part) of soil moisture content, and not for the wet soil.

L-band remote-sensing increases sampled levels of global soil moisture-air temperature coupling strength

Due to their shallow penetration of the soil column, remote-sensing (RS) soil moisture retrievals are often considered ill-suited for measuring the strength of soil moisture-air temperature coupling. Consequently, soil moisture proxies derived from antecedent rainfall considerations are commonly applied in their place. However, the suitably of satellite RS products has not yet been examined for newer soil moisture products derived from L-band microwave radiometry. This study globally compares correlations between monthly soil moisture and the monthly number of summertime hot days (NHD) for the case of three separate RS-based soil moisture products and a fourth soil moisture proxy derived from the standard precipitation index (SPI). Compared with SPI-based estimates, C- and X-band RS soil moisture products demonstrate a significantly (at p = 0.05 [-] confidence) weaker correlation with NHD. However, 2010–2018 L-band Soil Moisture and Ocean Salinity (SMOS) based soil moisture-NHD correlation is generally comparable to the SPI case. Furthermore, utilizing higher-precision 2015–2018 soil moisture products from the L-band Soil Moisture Active and Passive (SMAP) mission further strengthens soil moisture-NHD correlation and leads to stronger sampled correlations than SPI over global hot-spot regions (significant at p = 0.05 [-] confidence). Combined with the general equivalence of monthly surface and root zone soil moisture anomalies, these results suggest that the signal-to-noise ratio (SNR, i.e. the relative size of soil moisture signal and random observation error variances) of RS-based surface soil moisture product, instead of their vertical measurement depth, is the key limiting factor determining their ability to quantify land-atmosphere coupling strengths. Based on this, we argue that L-band soil moisture products have reached a sufficient level of SNR to be of value for the study of land-atmosphere coupling.

Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada

AbstractFuscopannaria leucostictais a rare and understudied cyanolichen with an interesting and unusual distribution in tertiary relict hotspots worldwide. There is a relatively large population in eastern North America, where it occurs mostly throughout the Appalachian Mountains and reaches its northernmost extent in New Brunswick and Nova Scotia, Canada. The ability to detect this species, and thus determine its habitat requirements, is critical for understanding how it might be affected by human-induced environmental degradation. Maximum entropy modelling with MaxEnt was used to predict the distribution of suitable habitat for this species in Nova Scotia using 62 presence locations, 1405 pseudo-absence locations and four environmental covariates: depth to water table (a proxy for relative soil moisture), distance to the coast and mean annual temperature and precipitation. Our predictive maps identify important habitat features and areas of high suitability in Nova Scotia with an area under the curve value of 0·85. The predicted distribution of this lichen was most affected by temperature. This study elucidates locations as well as species-habitat relationships forF. leucosticta,providing land managers with baseline data that can aid in the discovery of additional populations and provide a better understanding of its ecological requirements which will support the development of sound conservation strategies for this rare lichen.

Climate Change and Drought: the Soil Moisture Perspective

We review the extensive and sometimes conflicting recent literature on drought changes under global warming. We focus on soil moisture deficits, which are indicative of associated impacts on ecosystems. Soil moisture is a key state variable of the land surface, reflecting complex interactions between the water, energy, and carbon cycles. Offline projections relying on soil moisture proxy metrics indicate dramatic future drought increases, often interpreted as primarily driven by warming-induced increases in evaporative demand. However, such results appear inconsistent with other trends in the land–atmosphere system, including soil moisture, vegetation, and evapotranspiration. Recent studies begin to explain these discrepancies, highlighting the importance of soil–vegetation–atmosphere coupling, unaccounted for in offline projections. Future changes in soil moisture droughts should preferably be assessed with prognostic model outputs rather than offline heuristics and be interpreted in the context of the coupled soil–vegetation–atmosphere system.

Developing a remotely sensed drought monitoring indicator for Morocco.

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from MODIS data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders groups including the Ministry of Agriculture and Department of Water in Morocco.

An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches.

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08). The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2.04), was similar to the use of the SM–based cluster method with known SM (RPD = 1.79–2.08). The predictive results (RPD = 1.87–2.06) demonstrated that the NSMI-–based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

Variability of soil moisture proxies and hot days across the climate regimes of Australia

AbstractThe frequency of extreme events such as heat waves are expected to increase due to the effect of climate change, particularly in semiarid regions of Australia. Recent studies have indicated a link between soil moisture deficits and heat extremes, focusing on the coupling between the two. This study investigates the relationship between the number of hot days (Tx90) and four soil moisture proxies (Standardized Precipitation Index, Antecedent Precipitation Index, Mount's Soil Dryness Index, and Keetch‐Byram Drought Index), and how the strength of this relationship changes across various climate regimes within Australia. A strong anticorrelation between Tx90 and each moisture index is found, particularly for tropical savannas and temperate regions. However, the magnitude of the increase in Tx90 with decreasing moisture is strongest in semiarid and arid regions. It is also shown that the Tx90‐soil moisture relationship strengthens during the El Niño phases of El Niño–Southern Oscillation in regions which are more sensitive to changes in soil moisture.

Age, Spatial, and Temporal Variations in Hospital Admissions with Malaria in Kilifi County, Kenya: A 25-Year Longitudinal Observational Study.

BackgroundEncouraging progress has been seen with reductions in Plasmodium falciparum malaria transmission in some parts of Africa. Reduced transmission might lead to increasing susceptibility to malaria among older children due to lower acquired immunity, and this has implications for ongoing control strategies.Methods and FindingsWe conducted a longitudinal observational study of children admitted to Kilifi County Hospital in Kenya and linked it to data on residence and insecticide-treated net (ITN) use. This included data from 69,104 children aged from 3 mo to 13 y admitted to Kilifi County Hospital between 1 January 1990 and 31 December 2014. The variation in malaria slide positivity among admissions was examined in logistic regression models using the following predictors: location of the residence, calendar time, the child’s age, ITN use, and the enhanced vegetation index (a proxy for soil moisture). The proportion of malaria slide-positive admissions declined from 0.56 (95% confidence interval [CI] 0.54–0.58) in 1998 to 0.07 (95% CI 0.06–0.08) in 2009 but then increased again through to 0.24 (95% CI 0.22–0.25) in 2014. Older children accounted for most of the increase after 2009 (0.035 [95% CI 0.030–0.040] among young children compared to 0.22 [95% CI 0.21–0.23] in older children). There was a nonlinear relationship between malaria risk and prevalence of ITN use within a 2 km radius of an admitted child’s residence such that the predicted malaria positive fraction varied from ~0.4 to <0.1 as the prevalence of ITN use varied from 20% to 80%. In this observational analysis, we were unable to determine the cause of the decline in malaria between 1998 and 2009, which pre-dated the dramatic scale-up in ITN distribution and use.ConclusionFollowing a period of reduced transmission, a cohort of older children emerged who have increased susceptibility to malaria. Further reductions in malaria transmission are needed to mitigate the increasing burden among older children, and universal ITN coverage is a promising strategy to achieve this goal.

Historic drought puts the brakes on earthflows in Northern California

AbstractCalifornia's ongoing, unprecedented drought is having profound impacts on the state's resources. Here we assess its impact on 98 deep‐seated, slow‐moving landslides in Northern California. We used aerial photograph analysis, satellite interferometry, and satellite pixel tracking to measure earthflow velocities spanning 1944–2015 and compared these trends with the Palmer Drought Severity Index, a proxy for soil moisture and pore pressure that governs landslide motion. We find that earthflow velocities reached a historical low in the 2012–2015 drought, but that their deceleration began at the turn of the century in response to a longer‐term moisture deficit. Our analysis implies depth‐dependent sensitivity of earthflows to climate forcing, with thicker earthflows reflecting longer‐term climate trends and thinner earthflows exhibiting less systematic velocity variations. These findings have implications for mechanical‐hydrologic interactions that link landslide movement with climate change as well as sediment delivery in the region.

Evolution of a parsimonious rainfall–runoff model using soil moisture proxies

Pre-storm soil moisture conditions play a crucial role in the analysis of the physical processes involved in conceptual rainfall–runoff modeling and for demonstration of the soil–water–atmosphere interaction. Runoff quantification is of common interest to most hydrologists who study rainfall–runoff modeling. The scientific community prefers hydrological models with a smaller number of parameter requirements and superior performance, because of the increased water resource applications. In order to circumvent the impacts of sudden undesirable jumps (e.g., in the conventional CN model) in runoff estimation, we investigated the integral effects of soil moisture proxies before and after rainfall occurrence so as to reduce the structural inconsistencies of the previous models. Accordingly, these sudden jumps were avoided by incorporating a new expression that varied from storm to storm and with prior rainfall. Similarly, the utilization of additional information to calibrate new parameters reduced the applicability to ungauged watersheds. By persisting with the principle of simplicity and avoiding over-parameterization, a new model was developed and validated using measured data of 1804 storm rainfall–runoff events from 39 South Korean watersheds. The significant results exhibited by the suggested one-parameter model in comparison to those of the other contenders were hydrologically justified using three statistical metrics and scatter plots.

Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient

Vegetation is a major environmental factor influencing habitat selection in bird species. High resolution mapping of vegetation cover is essential to model the distribution of populations and improve the management of breeding habitats. However, the task is challenging for grassland birds because microhabitat variations relevant at the territory scale cannot be measured continuously over large areas to delineate areas of higher suitability. Remote sensing may help to circumvent this problem. We addressed this issue by using SPOT 5 imagery and phytosociological data. We mapped grassland vegetation in a floodplain using two methods. We (i) mapped the continuous Ellenberg index of moisture and (ii) identified 5 vegetation classes distributed across the wetness gradient. These two methods produced consistent output maps, but they also provided complementary results. Ellenberg index is a valuable proxy for soil moisture while the class approach provided more information about vegetation structure, and possibly trophic resources. In spite of the apparent uniformity of meadows, our data show that birds do not settle randomly along the moisture and vegetation gradients. Overall birds tend to avoid the driest vegetation classes, i.e. the highest grounds. Thus, vegetation maps based on remote sensing could be valuable tools to study habitat selection and niche partition in grassland bird communities. It is also a valuable tool for conservation and habitat management.

Assessing the impact of climate change on the frequency of floods in the Red River basin

The impact of climate change on the frequency distribution of spring floods in the Red River basin is investigated. Several major floods in the last couple of decades have caused major damages and inconvenience to people living in the Red River flood plain south of Winnipeg, and have raised the question of whether climate change is at least partly responsible for what appears to be more frequent occurrences of high spring runoff. To investigate whether this is the case, a regression model is used to associate spring peak flow at the US–Canada border with predictor variables that include antecedent precipitation in the previous fall (used as a proxy for soil moisture at freeze-up), winter snow accumulation and spring precipitation. Data from the Coupled Model Intercomparison Project – Phase 5 (CMIP5) are used to derive information about possible changes to the predictor variables in the future, and this information is then used to derive flood distributions for future climate conditions. While mean monthly precipitation during the winter months is expected to increase, winters are expected to be shorter in warmer climates and evaporation losses are expected to be higher, resulting in a net reduction in average snow pack accumulations. On the other hand, precipitation during the active snowmelt period is expected to increase. The average of future flood distributions obtained from an ensemble of 16 climate models is close to the distribution fitted to observed data, but there is considerable uncertainty surrounding the average, highlighting the difficulty in assessing changes in the frequency of extreme events.

A spatially coherent global soil moisture product with improved temporal resolution

Global soil moisture products that are completely independent of any type of ancillary data and solely rely on satellite observations are presented. Additionally, we further develop an existing downscaling technique that enhances the spatial resolution of such products to approximately 11 km. These products are based on internal modules of the Land Parameter Retrieval Model (LPRM), an algorithm that uses the radiative transfer equation to link soil moisture, vegetation optical depth and land surface temperature to observed brightness temperatures. The soil moisture product that is independent of any type of ancillary data uses the internally calculated dielectric constant as a soil moisture proxy. This data product is not influenced by errors associated with coarse-scale global soil property maps or by any other type of forcing (e.g. re-analysis) data and is therefore solely based on satellite microwave observations. The second step builds upon recent developments to increase the spatial resolution of the LPRM retrievals using a smoothing filter downscaling method. With this method we can attain a spatial resolution that can be more useful at the scale of local and regional hydrological studies as well. The steps presented in this paper were applied to observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The newly derived data sets were validated using ground-based observations from the International Soil Moisture Network (ISMN). The internally calculated dielectric constant product results in significantly more days with valid retrievals than the original soil moisture data products, in particular over arid regions. The dielectric constant product resulted in similar correlations with in situ data as the original soil moisture data product. Together, these findings demonstrate the usefulness of this new dielectric constant product for the hydrological modeling community and climate studies. A case study on the Australian Fitzroy catchment demonstrated that the downscaled data product has a more detailed spatial description of soil moisture, especially during wet and dry conditions with more pronounced dry and wet regions within the catchment. The increased resolution data products could therefore improve runoff predictions and this study demonstrated the potential added value of a transitioning from a spatial resolution of 56 km toward a higher resolution of 11 km. The hydrological implications of these newly developed data records are not only linked to AMSR-E satellite data, but also to the next generation Soil Moisture Active and Passive (SMAP) mission where a 9 km spatial resolution is the target resolution for satellite soil moisture products. The new data products will not replace the current LPRM products, but will be added to the existing array of data products and will become publicly available through our data portals.

Assessing the impacts of climate variability and human activities on annual runoff in the Luan River basin, China

Regional hydrological processes have been greatly influenced by human activities and climate variability. The inflow of Panjiakou Reservoir, which is the largest reservoir located on the Luan River, has shown a significant decreasing trend over the past 50 years. A large-scale hydrological model, namely the geomorphology-based hydrological model (GBHM), and a climate elasticity model were applied to perform quantitative attributing analysis of runoff change in the study catchment. Annual runoff decreased by 19.5 mm from 65.7 mm in the period 1956–1979 to 46.2 mm in the period 1980–2005. Annual direct water intake increased by 22.5 mm from 3.6 to 26.1 mm. Climate impact was accountable for the runoff increase of 8.8 and 9.2 mm simulated by GBHM and the climate elasticity model, respectively. Impacts of land use and vegetation change accounted for the runoff decrease of 2.5 mm. Change of precipitation and vegetation cover contributed to annual runoff change for the upper catchment (grassland-dominated). Change of antecedent precipitation (a proxy of soil moisture) also contributed to annual runoff change for the lower catchment (forest-dominated) and the whole catchment (mixture vegetation).

Filling the gap: A compositional gap regeneration model for managed northern hardwood forests

Regeneration of trees in canopy gaps created by timber harvest is vital for the sustainability of many managed forests. In northern hardwood forests of the Great Lakes region of North America, regeneration density and composition are highly variable because of multiple drivers that include browsing by herbivores, seed availability, and physical characteristics of forest gaps and stands. The long-term consequences of variability in regeneration for economic productivity and wildlife habitat are uncertain. To better understand and evaluate drivers and long-term consequences of regeneration variability, simulation models that combine statistical models of regeneration with established forest growth and yield models are useful. We present the structure, parameterization, testing and use of a stochastic, regression-based compositional forest gap regeneration model developed with the express purpose of being integrated with the US Forest Service forest growth and yield model ‘Forest Vegetation Simulator’ (FVS) to form an integrated simulation model. The innovative structure of our regeneration model represents only those trees regenerating in gaps with the best chance of subsequently growing into the canopy (i.e., the tallest). Using a multi-model inference (MMI) approach and field data collected from the Upper Peninsula of Michigan we find that ‘habitat type’ (a proxy for soil moisture and nutrients), deer density, canopy openness and basal area of mature ironwood (Ostrya virginiana) in the vicinity of a gap drive regeneration abundance and composition. The best model from our MMI approach indicates that where deer densities are high, ironwood appears to gain a competitive advantage over sugar maple (Acer saccharum) and that habitat type is an important predictor of overall regeneration success. Using sensitivity analyses we show that this regeneration model is sufficiently robust for use with FVS to simulate forest dynamics over long time periods (i.e., 200 years).

On the temporal clustering of US floods and its relationship to climate teleconnection patterns

AbstractThis article examines whether the temporal clustering of flood events can be explained in terms of climate variability or time‐varying land‐surface state variables. The point process modelling framework for flood occurrence is based on Cox processes, which can be represented as Poisson processes with randomly varying rate of occurrence. In the special case that the rate of occurrence is deterministic, the Cox process simplifies to a Poisson process. Poisson processes represent flood occurrences which are not clustered. The Cox regression model is used to examine the dependence of the rate of occurrence on covariate processes. We focus on 41 stream gauge stations in Iowa, with discharge records covering the period 1950–2009. The climate covariates used in this study are the North Atlantic Oscillation (NAO) and the Pacific/North American Teleconnection (PNA). To examine the influence of land‐surface forcing on flood occurrence, the antecedent 30 d rainfall accumulation is considered. In 27 out of 41 stations, either PNA or NAO, or both are selected as significant predictors, suggesting that flood occurrence in Iowa is influenced by large‐scale climate indices. Antecedent rainfall, used as a proxy for soil moisture, plays an important role in driving the occurrence of flooding in Iowa. These results point to clustering as an important element of the flood occurrence process. Copyright © 2012 Royal Meteorological Society

Modeling Valley Fever Incidence in Arizona Using a Satellite-Derived Soil Moisture Proxy

Valley Fever is caused by inhalation of spores from the soil-dwelling fungus Coccidioides spp. Pima, Pinal, and Maricopa counties, Arizona, have the highest Valley Fever incidence on earth. Despite reported links between climate, habitat, disease timing, and seasonality, relationships between the fungus and its putative affinity to moist soils are poorly understood. We used Normalized Difference Vegetation Index (NDVI) time series from the Advanced Very High Resolution Radiometer (AVHRR) sensor to compare soil moisture variations with disease incidence. Results suggest moist soils in the early spring, resulting from antecedent winter precipitation, correlate with increased incidence in these counties up to a year later.