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Measuring the spatiotemporal dynamics of lake and reservoir water storage is fundamental for assessing the influence of climate variability and anthropogenic activities on water quantity and quality. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only a few hundred lakes worldwide and cannot estimate absolute (i.e. total volume) water storage. We increased the number of measured lakes by a factor of 300 by using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry, in addition to radar altimetry from the Topex/Poseidon; Jason-1, Jason-2 and Jason-3; and Sentinel-3 and Sentinel-6 instruments. Historical time series (1984–2020) of water storage could be derived for more than 170 000 lakes globally with a surface area of at least 1 km 2 , representing 99 % of the total volume of all water stored in lakes and reservoirs globally. Specifically, absolute lake volumes are estimated based on topographic characteristics and lake properties that can be observed by remote sensing. In addition to that, we also generated relative lake volume changes solely based on satellite-derived heights and extents if both were available. Within this dataset, we investigated how many lakes can be measured in near real time (2020–current) in basins worldwide. We developed an automated workflow for near-real-time global lake monitoring of more than 27 000 lakes. The GloLakes historical and near-real-time lake storage dynamics data from 1984 to current are publicly available through 10.25914/K8ZF-6G46 (Hou et al., 2022c) and a web-based data explorer (http://www.globalwater.online , last access: 12 December 2023). [ABSTRACT FROM AUTHOR]

Measurements of the spatiotemporal dynamics of lake and reservoir water storage are fundamental in the assessment of the influence of climate variability and anthropogenic activities on water quantity and quality, as well as wetland ecology and the estimating greenhouse gas emissions from lakes. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only few hundreds of lakes worldwide. In this study, the number of measured lakes was increased by a factor 400 using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry in addition to radar altimetry from the Topex/Poseidon, Jason-1, -2 and -3, and Sentinel-3 instruments. Time series of relative (i.e., storage change) or absolute (i.e., total stored volume) storage for more than 170,000 lakes globally with a surface area of at least 1 km² (representing 99% of the total volume of all water stored in lakes and reservoirs globally) were retrieved. Within these, we were able to develop an automated workflow for near real-time global lake monitoring of more than 27,000 lakes. The historical and near real-time lake storage dynamics data for 1984 to current are publicly available through https://doi.org/10.25914/K8ZF-6G46 (Hou et al., 2022). [ABSTRACT FROM AUTHOR]

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx. [ABSTRACT FROM AUTHOR]

Hydromorphological attributes such as flow width, water extent, and gradient play an important role in river hydrological, biogeochemical, and ecological processes and can help to predict river conveyance capacity, discharge, and flow routing. While there are some river width datasets at global or regional scales, they do not consider temporal variation in river width and do not cover all Australian rivers. We combined detailed mapping of 1.4 million river reaches across the Australian continent with inundation frequency mapping from 27 years of Landsat observations. From these, the average flow width at different recurrence frequencies was calculated for all reaches, having a combined length of 3.3 million km. A parameter γ was proposed to describe the shape of the frequency–width relationship and can be used to classify reaches by the degree to which flow regime tends towards permanent, frequent, intermittent, or ephemeral. Conventional scaling rules relating river width to gradient and contributing catchment area and discharge were investigated, demonstrating that such rules capture relatively little of the real-world variability. Uncertainties mainly occur in multi-channel reaches and reaches with unconnected water bodies. The calculated reach attributes are easily combined with the river vector data in a GIS, which should be useful for research and practical applications such as water resource management, aquatic habitat enhancement, and river engineering and management. The dataset is available at 10.25914/5c637a7449353 (Hou et al., 2019). [ABSTRACT FROM AUTHOR]

Hydromorphological attributes such as flow width, water extent and gradient play an important role in river hydrological, biogeochemical and ecological processes; helping to predict river conveyance capacity, discharge and flow routing. While there are some river width datasets at global or regional scales, they do not consider temporal variation in river width and do not cover all Australian rivers. We combined detailed mapping of 1.4 million river reaches across the Australian continent with inundation frequency mapping from 27-years of Landsat observations. From these, the average flow width at different recurrence frequencies was calculated for all reaches, having a combined length of 3.3 million km. A parameter γ was proposed to describe the shape of the frequency-width relationship and can be used to classify reaches by the degree to which flow regime tends towards permanent, frequent, intermittent or ephemeral. Proposed scaling rules relating river width to gradient and contributing catchment area and discharge were investigated, demonstrating that such rules capture relatively little of real-world variability. Uncertainties mainly occur in multi-channel reaches and reaches with unconnected water bodies. The calculated reach attributes are easily combined with the river vector data in GIS, which should be useful for research and practical applications such as water resource management, aquatic habitat enhancement, and river engineering and management. The dataset is available at https://doi.org/10.25914/5c637a7449353 (Hou et al. 2019). [ABSTRACT FROM AUTHOR]

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologie applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation--Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP-Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Re-analyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product. [ABSTRACT FROM AUTHOR]

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable's scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities. [ABSTRACT FROM AUTHOR]

In a recent paper in this journal, Bradshaw and colleagues analyse country statistics on flood characteristics, land cover and land cover change, and conclude that deforestation amplifies flood risk and severity in the developing world. The study addresses an important and long-standing question, but we identify important flaws. Principal among these are difficulties in interpreting country statistics and the correlation between population and floods. We review current knowledge, which suggests that the removal of trees does not affect large flood events, although associated landscape changes can under some circumstances. Reanalysis of the data analysed by Bradshaw and colleagues shows that population density alone already explains up to 83% of the variation in reported flood occurrences, considerably more than forest cover or deforestation (<10%). Feasible explanations for this statistical finding – whether spurious or causative – are not difficult to conceive. We, therefore, consider the conclusion of Bradshaw and colleagues to be unsupported. However, their study is a valuable first step to show how these or similar flood data might be used to further explore the relationship between land cover and flooding. [ABSTRACT FROM AUTHOR]

The net ecosystem exchange (NEE) of forests represents the balance of gross primary productivity (GPP) and respiration (R). Methods to estimate these two components from eddy covariance flux measurements are usually based on a functional relationship between respiration and temperature that is calibrated for night-time (respiration) fluxes and subsequently extrapolated using daytime temperature measurements. However, respiration fluxes originate from different parts of the ecosystem, each of which experiences its own course of temperature. Moreover, if the temperature-respiration function is fitted to combined data from different stages of biological development or seasons, a spurious temperature effect may be included that will lead to overestimation of the direct effect of temperature and therefore to overestimates of daytime respiration. We used the EUROFLUX eddy covariance data set for 15 European forests and pooled data per site, month and for conditions of low and sufficient soil moisture, respectively. We found that using air temperature (measured above the canopy) rather than soil temperature (measured 5 cm below the surface) yielded the most reliable and consistent exponential (Q10) temperature-respiration relationship. A fundamental difference in air temperature-based Q10 values for different sites, times of year or soil moisture conditions could not be established; all were in the range 1.6–2.5. However, base respiration (R0, i.e. respiration rate scaled to 0 °C) did vary significantly among sites and over the course of the year, with increased base respiration rates during the growing season. We used the overall mean Q10 of 2.0 to estimate annual GPP and R. Testing suggested that the uncertainty in total GPP and R associated with the method of separation was generally well within 15%. For the sites investigated, we found a positive relationship between GPP and R, indicating that there is a latitudinal trend in NEE because the absolute decrease in GPP towards the pole is greater than in R. [ABSTRACT FROM AUTHOR]

This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection and false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multiobjective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices. Plain Language Summary: Deep neural networks have recently demonstrated great versatility and an unprecedented capacity to model complex problems. In weather modeling, these algorithms have been applied to solve different problems. This is a promising area of research, given the availability of large volumes of weather data and increasingly powerful computers. Neural network models can learn to solve problems based on a metric, which the model tries to optimize. However, the quality of weather models is measured using a large variety of metrics, which can be a challenge when choosing which metric the model should optimize. In the case of precipitation, categorical binary metrics are a popular choice to assess the quality of a model. These metrics reduce precipitation to a "yes" or "no" event, and the results of the predicting model can be compared with the actual observations. This method is simple, yet powerful and a large number of indices and statistics have been developed to assess different aspects of the quality of precipitation models. As precipitation models are commonly assessed using these categorical binary metrics, it would be very convenient to optimize models based on them. Unfortunately, the mathematical nature of these metrics makes them unsuitable for optimizing deep learning models. In this work we present an alternative formulation for these categorical binary indices which can be used to train models. We demonstrate how a deep learning model can be trained to generate better quality precipitation data. Key Points: A methodology for optimizing neural network models based on categorical binary indices is introducedA novel multiobjective loss function combining continuous and categorical binary indices is presentedThe experimental results are tested using well‐known metrics in weather analysis, such as ROC curves [ABSTRACT FROM AUTHOR]

Streamflow Q estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from 3000 to 4000 small-to-medium-sized catchments (10-10 000 km2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total, 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps because of their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (at 0.125° resolution). These maps possess several unique features: they represent observation-driven estimates, they are based on an unprecedentedly large set of catchments, and they have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macroscale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of four macroscale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available online (). [ABSTRACT FROM AUTHOR]

Spectral indices derived from satellite optical remote sensing data have typically been used for fire severity estimation, although other remote sensing systems such as Light Detection and Ranging (LiDAR) are increasingly applied. Despite a multitude of remotely sensed fire severity estimation methods, comparisons of method performance are few. Insights into the merits and limitations of remotely sensed fire severity methods help develop appropriate spatial tools for the management of fire-affected areas. We evaluated the performance of seven passive (optical) and active (LiDAR) remotely sensed fire severity estimation methods in classifying and explaining variation in a field-estimated modified Composite Burn Index (MCBI) for a recent large wildfire in south-eastern Australia. Our evaluation included three commonly applied indices; the differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR) and Relative Burn Ratio (RBR). We compared these NBR indices against two recently proposed fire severity estimation methods that have not previously been evaluated with CBI field data – the Vegetation Structure Perpendicular Index (VSPI) spectral index and the LiDAR point cloud-derived Profile Area Change (PAC), along with experimental relativized forms of these indices (RVSPI and RPAC, respectively). The RVSPI (κ = 0.47) demonstrated similar overall classification accuracy (N classes = 4) to the PAC (κ = 0.48), however both indices had lower classification accuracy than the dNBR (κ = 0.59), RdNBR (κ = 0.59) and RBR (κ = 0.61). The VSPI and PAC were unable to accurately represent non-structural changes caused by lower severity fire. Application of these optical and LiDAR indices should consider their discussed limitations in relation to the objectives of their application. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

Access to water is a critical aspect of livestock production, although the relationship between livestock weight gain and water quality remains poorly understood. Previous work has shown that water quality of poorly managed farm dams can be improved by fencing and constructing hardened watering points to limit stock access to the dam, and revegetation to filter contaminant inflow. Here we use cattle weight gain data from three North American studies to develop a cost-benefit analysis for the renovation of farm dams to improve water quality and, in turn, promote cattle weight gain on farms in south-eastern Australia. Our analysis indicated a strong likelihood of positive results and suggested there may be substantial net economic benefit from renovating dams in poor condition to improve water quality. The average per-farm Benefit-Cost Ratios based on deterministic assumptions was 1.5 for New South Wales (NSW) and 3.0 for Victoria in areas where rainfall exceeds 600mm annually. Our analyses suggested that cattle on farms in NSW and Victoria would need to experience additional weight gain from switching to clean water of at least 6.5% and 1.8% per annum respectively, to break even in present value terms. Monte Carlo simulation based on conservative assumptions indicated that the probability of per-farm benefits exceeding costs was greater than 70%. We recommend localised experiments to assess the impact of improved water quality on livestock weight gain in Australian conditions to confirm these expectations empirically. [ABSTRACT FROM AUTHOR]

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance. [ABSTRACT FROM AUTHOR]

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003-2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches. [ABSTRACT FROM AUTHOR]

Australia is frequently subject to droughts and floods. Its hydrology is strongly connected to oceanic and atmospheric oscillations (climate modes) such as the El Niño-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM). A global 32-yr dataset of remotely sensed surface soil moisture (SSM) was used to examine hydrological variations in mainland Australia for the period 1978-2010. Complex empirical orthogonal function (CEOF) analysis was applied to extract independent signals and to investigate their relationships to climate modes. The annual cycle signal represented 46.3% of the total variance and a low but highly significant connection with SAM was found. Two multiannual signals with a lesser share in total variance (6.3% and 4.2%) were identified. The first one had an unstable period of 2-5 yr and reflected an east-west pattern that can be associated with ENSO and SAM but not with IOD. The second one, a 1- to 5-yr oscillation, formed a dipole pattern between the west and north and can be linked to ENSO and IOD. As expected, relationships with ENSO were found throughout the year and are especially strong during southern spring and summer in the east and north. Somewhat unexpectedly, SAM impacts strongest in the north and east during summer and is proposed as the key driver of the annual SSM signal. The IOD explains SSM variations in the north, east, and southeast during spring and also in the west during winter. [ABSTRACT FROM AUTHOR]

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global real-time drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system. [ABSTRACT FROM AUTHOR]

Satellite observations identify the Mongolian steppes as a hotspot of global biomass reduction, the extent of which is comparable with tropical rainforest deforestation. To conserve or restore these grasslands, the relative contributions of climate and human activities to degradation need to be understood. Here we use a recently developed 21-year (1988–2008) record of satellite based vegetation optical depth (VOD, a proxy for vegetation water content and aboveground biomass), to show that nearly all steppe grasslands in Mongolia experienced significant decreases in VOD. Approximately 60% of the VOD declines can be directly explained by variations in rainfall and surface temperature. After removing these climate induced influences, a significant decreasing trend still persists in the VOD residuals across regions of Mongolia. Correlations in spatial patterns and temporal trends suggest that a marked increase in goat density with associated grazing pressures and wild fires are the most likely non-climatic factors behind grassland degradation. [ABSTRACT FROM AUTHOR]