Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part I evaluation of a water and carbon balance model

Understanding and quantitatively evaluating the regional impacts of climate change and variability (e.g., droughts) on forest ecosystem functions (i.e., water yield, evapotranspiration, and productivity) and services (e.g., fresh water supply and carbon sequestration) is of great importance for developing climate change adaptation strategies for National Forests and Grasslands (NFs) in the United States. However, few reliable continental-scale modeling tools are available to account for both water and carbon dynamics. The objective of this study was to test a monthly water and carbon balance model, the Water Supply Stress Index (WaSSI) model, for potential application in addressing the influences of drought on NFs ecosystem services across the conterminous United States (CONUS). The performance of the WaSSI model was comprehensively assessed with measured streamflow (Q) at 72 U.S. Geological Survey (USGS) gauging stations, and satellite-based estimates of watershed evapotranspiration (ET) and gross primary productivity (GPP) for 170 National Forest and Grassland (NFs). Across the 72 USGS watersheds, the WaSSI model generally captured the spatial variability of multi-year mean annual and monthly Q and annual ET as evaluated by Correlation Coefficient (R = 0.71–1.0), Nash–Sutcliffe Efficiency (NS = 0.31–1.00), and normalized Root Mean Squared Error (0.06–0.48). The modeled ET and GPP by WaSSI agreed well with the remote sensing-based estimates for multi-year annual and monthly means for all the NFs. However, there were systemic discrepancies in GPP between our simulations and the satellite-based estimates on a yearly and monthly scale, suggesting uncertainties in GPP estimates in all methods (i.e., remote sensing and modeling). Overall, our assessments suggested that the WaSSI model had the capability to reconstruct the long-term forest watershed water and carbon balances at a broad scale. This model evaluation study provides a foundation for model applications in understanding the impacts of climate change and variability (e.g., droughts) on NFs ecosystem service functions

Regional features and seasonality of land – atmosphere coupling over Eastern China

Land–atmosphere coupling is a key process of the climate system, and various coupling mechanisms have been proposed before based on observational and numerical analyses. The impact of soil moisture (SM) on evapotranspiration (ET) and further surface temperature (ST) is an important aspect of such coupling. Using ERA-Interim data and CLM4.0 offline simulation results, this study further explores the relationships between SM/ST and ET to better understand the complex nature of the land–atmosphere coupling (i.e., spatial and seasonal variations) in eastern China, a typical monsoon area. It is found that two diagnostics of land–atmosphere coupling (i.e., SM–ET correlation and ST–ET correlation) are highly dependent on the climatology of SM and ST. By combining the SM–ET and ST–ET relationships, two “hot spots” of land–atmosphere coupling over eastern China are identified: Southwest China and North China. In Southwest China, ST is relatively high throughout the year, but SM is lowest in spring, resulting in a strong coupling in spring. However, in North China, SM is relatively low throughout the year, but ST is highest in summer, which leads to the strongest coupling in summer. Our results emphasize the dependence of land–atmosphere coupling on the seasonal evolution of climatic conditions and have implications for future studies related to land surface feedbacks

A potential predictor of multi - season droughts in Southwest China: soil moisture and its memory

During the last decade, several high intensity and long duration droughts happened in Southwest China (SWC) and resulted in tremendous socioeconomic losses. Meanwhile, it is well known that soil moisture (SM) plays a key role in land–atmosphere interaction and weather/climate prediction and is a direct drought index. Thus, a general analysis of SM is beneficial to drought research and prediction over this region. Based on the SM data of Global Land Data Assimilation System V2.0, we examined the temporal variations in SM in SWC during 1961–2012. Results show that significant soil drying trend happened in autumn accompanied by an evident abrupt change in 1991. Moreover, SM exhibits a strong and season-dependent persistence. Particularly, the autumn SM anomaly shows the strongest memory that can be sustained to the next spring. Along with the decadal shift of SM, the memory time of autumn SM can extend from 3 months before 1991 to 6 months in recent years. We further used the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales to identify the droughts in different seasons over SWC, and the inter-annual change patterns of autumn SM and SPEIs are generally in agreement with each other, which confirms that SM is suitable for indicating the droughts. Our results suggest that the autumn SM can be a potential predictor of persistent droughts over SWC, especially for those multi-season persistent drought events started in autumn

Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product

Variability of spring ecosystem water use efficiency in Northeast Asia and its linkage to the Polar-Eurasia pattern

Given that water use efficiency (WUE) is an important indicator to measure the trade-off between carbon uptake and water consumption within the ecosystem, better understanding the variation of ecosystem WUE and related driving factors is of great interest. In this study, the variability of spring ecosystem WUE in Northeast Asia (NEA) was investigated. The results show that its primary mode exhibits a monosign variation. This mode is directly controlled by the variability of gross primary productivity. The climate conditions also play remarkable roles, featuring that warm surface air temperature (high soil moisture) favors enhanced ecosystem WUE in northern (southern) NEA. Further analysis reveals that the Polar-Eurasia (POL) pattern can significantly impact the variability of spring ecosystem WUE in NEA through changing surface air temperature and soil moisture. When the POL pattern lies in the positive phase during spring, anticyclonic circulation anomalies with an equivalent barotropic structure prevail over northern NEA, concurrent with anomalous easterlies over southern NEA and a weakening of the East Asian jet (EAJ). Accordingly, anomalous downward motion is introduced over northern NEA, resulting in higher surface air temperature which is beneficial for the increase of local ecosystem WUE. Meanwhile, the easterly anomalies help to increase water vapor transport into southern NEA and the weakened EAJ can induce anomalous ascending over southern NEA, favoring the increase of precipitation and hence soil moisture, which consequently enhances the ecosystem WUE in southern NEA

Risk Assessment of Snow Disasters for Animal Husbandry on the Qinghai–Tibetan Plateau and Influences of Snow Disasters on the Well-Being of Farmers and Pastoralists

In the context of global warming, meteorological disasters occur more frequently in various regions which exert increasing influences on human life. Snow disasters are some of the natural disasters that most seriously affect the development of husbandry on the Qinghai–Tibetan Plateau (QTP), so it is necessary to explore their spatio-temporal variations and perform comprehensive risk assessment. Based on the daily snow depth data set in China, obtained by inversion of satellite remote sensing data, the spatio-temporal variation characteristics of snow disasters on the QTP from 1980 to 2019 were studied. The regional difference in the comprehensive risks of snow disasters for the husbandry on the QTP was evaluated from four perspectives, i.e., the risk of hazard factors, sensitivity of hazard-inducing environments, vulnerability of hazard-affected bodies, and disaster prevention and mitigation capacity. The farmer and pastoralist well-being (FPWB) index in five typical regions was constructed to discuss the possible influences of snow disasters on the FPWB since the 21st century. Results show that, in the last 40 years, the frequency, duration, average snow depth, and grade of snow disasters on the QTP all exhibited significant interannual and interdecadal variabilities, and they also displayed a declining long-term trend. The comprehensive risk of snow disasters for the husbandry on the QTP is low in the north while high in the south. The high-risk zone accounts for 1.54% of the total and is mainly located in Kashgar City in the north-western end of the QTP; the sub-high-risk and medium-risk zones are mainly found in the south of the plateau and are distributed in a tripole pattern, separately covering 15.96% and 16.32% of the total area of the plateau; the north of the plateau mainly belongs to low-risk and sub-low-risk zones, which separately account for 43.06% and 23.12% of the total area of the plateau. Since the beginning of the 21st century, the FPWB in five typical regions, namely, Kashgar (I), Shigatse (II), Nagqu (III), Qamdo (IV), and Yushu (V), has been increasing, while the risk of snow disasters has gradually decreased. Every 1% decrease in the risk of snow disasters corresponded to 0.186%, 0.768%, 0.378%, 0.109%, and 0.03% increases in the FPWB index in the five regions. Snow disasters affect FPWB mainly by directly or indirectly damaging material resources (livestock inventories and meat production) and social and financial resources

Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB

Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part II assessment results and management implications

The 781,000 km2 (193 million acre) United States National Forests and Grasslands system (NF) provides important ecosystem services such as clean water supply, timber production, wildlife habitat, and recreation opportunities to the American public. Quantifying the historical impacts of climate change and drought on ecosystem functions at the national scale is essential to develop sound forest management and watershed restoration plans under a changing climate. This study applied the previously validated Water Supply and Stress Index model (WaSSI) to 170 NFs in the conterminous U.S. (CONUS) to examine how historical extreme droughts have affected forest water yield (Q) and gross primary productivity (GPP). For each NF, we focused on the five years with the lowest annual SPI3 (Standardized Precipitation Index on a 3-month time scale) during 1962–2012. The extent of extreme droughts as measured by the number of NFs and total area affected by droughts has increased during the last decade. Across all lands in CONUS, the most extreme drought during the past decade occurred in 2002, resulting in a mean reduction of Q by 32% and GPP by 20%. For the 170 individual NFs, on average, the top-five droughts represented a reduction in precipitation by 145 mm yr−1 (or 22%), causing reductions in evapotranspiration by 29 mm yr−1 (or 8%), Q by 110 mm yr−1 (or 37%) and GPP by 65 gC m−2 yr−1 (or 9%). The responses of the forest hydrology and productivity to the top-five droughts varied spatially due to different land-surface characteristics (e.g., climatology and vegetation) and drought severity at each NF. This study provides a comprehensive benchmark assessment of likely drought impacts on the hydrology and productivity in NFs using consistent methods and datasets across the conterminous U.S. The study results are useful to the forestry decision makers for developing appropriate strategies to restore and protect ecosystem services in anticipating potential future droughts and climate change