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Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

Agricultural and Forest Meteorology↗

Operationalizing crop model data assimilation for improved on-farm situational awareness

The ability of ‘digital agriculture’ to support on-farm decision making is predicated on the real-time combination of observations and prior knowledge into an integrated digital environment. The mathematical discipline that seeks to provide this integration is known as model data assimilation (DA), with demonstrated benefits including improved predictive reliability, and the capacity to identify unexpected changes in field conditions and potential measurement errors. Despite routine adoption in other fields, the delayed adoption of DA in agriculture is due to the need to express end-of-season outcomes such as yield, update forecasts of these outcomes throughout the growing season as data become available, and enhance forecast reliability. To overcome these challenges, three guiding principles are introduced, providing a means to operationalize crop model DA for robust on-farm decision support. We apply the guiding principles using a South Australian viticulture case study. Our case study involves application of an iterative form of a widely used DA algorithm (ensemble Kalman filter) to dynamically update both static parameters and states associated with a grapevine simulation model. Daily weather data as well as fortnightly ground-based leaf area index (LAI) data are used for assimilation. It is shown how crop model DA can lead to not only significant improvements in forecasts of LAI but also to forecasts of end-of-season yield. The guiding principles also enable observations of greatest value to be identified throughout the season. This study highlights the role that formal crop model DA can play in agricultural decision support through enhancing situational awareness in real time.

Agricultural and Forest Meteorology↗

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗

Network of networks: Time series clustering of AmeriFlux sites

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Agricultural and Forest Meteorology↗

Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico↗

Lowland tropical forests remain a methane sink under warming and long-term hurricane disturbance recovery

Methane (CH 4 ) is a potent greenhouse gas, and tropical forests account for roughly one–third of global atmospheric CH 4 uptake by soils. Projected warming and more frequent hurricanes in these ecosystems may alter soil CH 4 sink strength, as warmer and wetter soils enhance methanogenesis activity. We measured soil CH 4 and CO 2 efflux during the calendar summer months of 2023 and 2024 alongside continuous records of soil moisture, soil and air temperature, and precipitation in an in–situ warming experiment (TRACE) located in a lowland tropical forest in Puerto Rico, six to seven years after Hurricanes Irma and Maria (2017). The realized warming (∼1.95°C) enhanced soil respiration only in summer 2023 ( p < 0.05), but net soil CH 4 uptake was invariant in both campaigns ( p > 0.05). Instead, sampling day and between–plot variability explained soil CH 4 dynamics much more than treatment contrasts. Importantly, CH 4 uptake was consistently coupled to CO 2 efflux, suggesting tight linkages between methanotrophic and heterotrophic activities. Between treatments, CH 4 and CO 2 responses to soil temperature variation were less sensitive in warmed plots, which may suggest weak metabolic upregulation under elevated temperatures. Together, these findings indicate that lowland tropical soils remain CH 4 sink even under warming and years after hurricane disturbance, with CH 4 dynamics driven more by spatial and temporal variability than experimental warming. Long–term, high–resolution monitoring integrating soil biogeochemistry and microbial processes will be critical to determine whether the observed net CH 4 uptake signal represents a sustainable or transient response under continued warming and disturbance.

Puerto Rico, Sabana Field Research Station↗

Energy budget above a high-elevation subalpine forest in complex topography

Components of the energy budget were measured above a subalpine coniferous forest over two complete annual cycles. Sensible and latent heat fluxes were measured by eddy covariance. Bowen ratios ranged from 0.7 to 2.5 in the summer (June–September) depending upon the availability of soil water, but were considerably higher (∼3–6) during winter (December–March). Energy budget closure averaged better than 84% on a half-hourly basis in both seasons with slightly greater closure during the winter months. The energy budget showed a dependence on friction velocity ( u ∗ ), approaching complete closure at u ∗ values greater than 1 m s −1 . The dependence of budget closure on u ∗ explained why energy balance was slightly better in the winter as opposed to summer, since numerous periods of high turbulence occur in winter. It also explained the lower degree of energy closure (∼10% less) during easterly upslope flow since these periods were characterized by low wind speeds ( U <4 m s −1 ) and friction velocities ( u &#x2217; &lt;0.5 "> u∗<0.5 m s −1 ). Co-spectral analysis suggests a shift of flux density towards higher frequencies under conditions where closure was obtained. It is suggested that low frequency contributions to the flux and advection were responsible for the lack of day-time energy budget closure. These effects were reduced at high friction velocities observed at our site. Our ability to close the energy budget at night was also highly dependent on friction velocity, approaching near closure (∼90%) at u ∗ values between 0.7 and 1.1 m s −1 . Below this range, the airflow within the canopy becomes decoupled with the flow above. Above this range, insufficient temperature resolution of the sonic anemometer obscured the small temperature fluctuations, rendering measurements intractable.

Agricultural and Forest Meteorology↗

Partitioning of water flux in a Sierra Nevada ponderosa pine plantation

The weather patterns of the west side of the Sierra Nevada Mountains (cold, wet winters and hot, dry summers) strongly influence how water is partitioned between transpiration and evaporation and result in a specific strategy of water use by ponderosa pine trees (Pinus ponderosa) in this region. To investigate how year-round water fluxes were partitioned in a young ponderosa pine ecosystem in the Sierra Nevada Mountains, water fluxes were continually measured from June 2000 to May 2001 using a combination of sap flow and eddy covariance techniques (above- and below-canopy). Water fluxes were modeled at our study site using a biophysical model, FORFLUX. During summer and fall water fluxes were equally partitioned between transpiration and soil evaporation while transpiration dominated the water fluxes in winter and spring. The trees had high rates of canopy conductance and transpiration in the early morning and mid-late afternoon and a mid-day depression during the dry season. We used a diurnal centroid analysis to show that the timing of high canopy conductance and transpiration relative to high vapor pressure deficit (D) shifted with soil moisture: during periods of low soil moisture canopy conductance and transpiration peaked early in the day when D was low. Conversely, during periods of high soil moisture canopy conductance and transpiration peaked at the same time or later in the day than D. Our observations suggest a general strategy by the pine trees in which they maximize stomatal conductance, and therefore carbon fixation, throughout the day on warm sunny days with high soil moisture (i.e. warm periods in winter and late spring) and maximize stomatal conductance and carbon fixation in the morning through the dry periods. FORFLUX model estimates of evaporation and transpiration were close to measured/calculated values during the dry period, including the drought, but underestimated transpiration and overestimated evaporation during the wet period. ?? 2003 Elsevier Science B.V. All rights reserved.

Agricultural and Forest Meteorology↗

Climate warming could reduce runoff significantly in New England, USA

The relation between mean annual temperature (MAT), mean annual precipitation (MAP) and evapotranspiration (ET) for 38 forested watersheds was determined to evaluate the potential increase in ET and resulting decrease in stream runoff that could occur following climate change and lengthening of the growing season. The watersheds were all predominantly forested and were located in eastern North America, along a gradient in MAT from 3.5??C in New Brunswick, CA, to 19.8??C in northern Florida. Regression analysis for MAT versus ET indicated that along this gradient ET increased at a rate of 2.85 cm??C-1 increase in MAT (??0.96 cm??C-1, 95% confidence limits). General circulation models (GCM) using current mid-range emission scenarios project global MAT to increase by about 3??C during the 21st century. The inferred, potential, reduction in annual runoff associated with a 3??C increase in MAT for a representative small coastal basin and an inland mountainous basin in New England would be 11-13%. Percentage reductions in average daily runoff could be substantially larger during the months of lowest flows (July-September). The largest absolute reductions in runoff are likely to be during April and May with smaller reduction in the fall. This seasonal pattern of reduction in runoff is consistent with lengthening of the growing season and an increase in the ratio of rain to snow. Future increases in water use efficiency (WUE), precipitation, and cloudiness could mitigate part or all of this reduction in runoff but the full effects of changing climate on WUE remain quite uncertain as do future trends in precipitation and cloudiness.

Agricultural and Forest Meteorology↗

Airflows and turbulent flux measurements in mountainous terrain: Part 1. Canopy and local effects

We have studied the effects of local topography and canopy structure on turbulent flux measurements at a site located in mountainous terrain within a subalpine, coniferous forest. Our primary aim was to determine whether the complex terrain of the site affects the accuracy of eddy flux measurements from a practical perspective. We observed displacement heights, roughness lengths, spectral peaks, turbulent length scales, and profiles of turbulent intensities that were comparable in magnitude and pattern to those reported for forest canopies in simpler terrain. We conclude that in many of these statistical measures, the local canopy exerts considerably more influence than does topographical complexity. Lack of vertical flux divergence and modeling suggests that the flux footprints for the site are within the standards acceptable for the application of flux statistics. We investigated three different methods of coordinate rotation: double rotation (DR), triple rotation (TR), and planar-fit rotation (PF). Significant variability in rotation angles at low wind speeds was encountered with the commonly used DR and TR methods, as opposed to the PF method, causing some overestimation of the fluxes. However, these differences in fluxes were small when applied to large datasets involving sensible heat and CO 2 fluxes. We observed evidence of frequent drainage flows near the ground during stable, stratified conditions at night. Concurrent with the appearance of these flows, we observed a positive bias in the mean vertical wind speed, presumably due to subtle topographic variations inducing a flow convergence below the measurement sensors. In the presence of such drainage flows, advection of scalars and non-zero bias in the mean vertical wind speed can complicate closure of the mass conservation budget at the site.

Agricultural and Forest Meteorology↗

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet ( Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Agricultural and Forest Meteorology↗

Modeling groundwater nitrate concentrations in private wells in Iowa

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square = 0.77) and was acceptable in the testing set (r-square = 0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.

Iowa↗

Simulation of streamflow and water quality in the Christina River subbasin and overview of simulations in other subbasins of the Christina River Basin, Pennsylvania, Maryland, and Delaware, 1994-98

The Christina River Basin drains 565 square miles (mi 2 ) in Pennsylvania and Delaware and includes the major subbasins of Brandywine Creek, Red Clay Creek, White Clay Creek, and Christina River. The Christina River subbasin (exclusive of the Brandywine, Red Clay, and White Clay Creek subbasins) drains an area of 76 mi 2 . Streams in the Christina River Basin are used for recreation, drinking water supply, and support of aquatic life. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the stream. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point- and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in nonpoint-source evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program–Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in small subbasins predominantly covered by one land use following a nonpoint- source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at two sites in the Christina River subbasin and nine sites elsewhere in the Christina River Basin. The HSPF model for the Christina River subbasin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into nine reaches draining areas that ranged from 3.8 to 21.9 mi 2 . Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the Christina River subbasin are residential, urban, forested, agricultural, and open. The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Daily precipitation data from one National Oceanic and Atmospheric Administration (NOAA) meteorologic station and hourly data from one NOAA meteorologic station were used for model input. The difference between observed and simulated streamflow volume ranged from -2.3 to 5.3 percent for a 10-month portion of the calibration period at the two calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error for the 4-year period. For example, at Christina River at Coochs Bridge, near the bottom of the free-flowing part of the subbasin (drainage area of 21 mi 2 ), annual differences between observed and simulated streamflow ranged from -6.9 to 6.5 percent and the overall error for the 4-year period was -1.1 percent. Calibration errors for 36 storm periods at the three calibration sites for total volume, low-flow recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data. The water-quality component of the model was calibrated using nonpoint-source monitoring data collected at two USGS streamflow-measurement stations and other water-quality monitoring data. The period of record for water-quality monitoring was variable at the stations, with a start date ranging from October 1994 to January 1998 and an end date of October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended-solids data may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulaion. Comparison of observed to simulated loads for up to six storms in 1998 at the two nonpoint-source monitoring sites (Little Mill Creek near Newport and Christina River at Coochs Bridge, Del.) indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria; much larger errors are possible for individual events. Assessment of the water-quality calibration under stormflow conditions is limited by the relatively small amount of available water-quality data in the subbasin. Users of the Christina River subbasin HSPF model and HSPF models for other subbasins in the Christina River Basin should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow-duration curves suggest the model simulates streamflow reasonably well when measured over a broad range of conditions and time although streamflow and the corresponding water quality for individual storm events may not be well simulated; streamflow-duration curves for the simulation period compare well with duration curves for the 8-year period ending in 2001 at Christina River at Coochs Bridge, Del., and include all but the extreme high-flow and low-flow events; and calibration for water quality was based on limited data, with the result of increasing uncertainty in the water-quality simulation.

Water-Resources Investigations Report↗

Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98

The Christina River Basin drains 565 square miles (mi 2 ) in Pennsylvania, Maryland, and Delaware. Water from the basin is used for recreation, drinking water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, White Clay Creek, and Red Clay Creek. The White Clay Creek is the second largest of the subbasins and drains an area of 108 mi 2 . Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in non point-source evaluation, four independent models, one for each of the three major subbasins and for the Christina River, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base- flow samples were collected during 1998 at two sites in the White Clay Creek subbasin and at nine sites in the other subbasins. The HSPF model for the White Clay Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 17 reaches draining areas that ranged from 1.37 to 13 mi 2 . Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the White Clay Creek Basin are agricultural, forested, residential, and urban. The hydrologic component of the model was run at an hourly time step and primarily calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Additional calibration was done using data from two other USGS streamflow-measurement stations with periods of record shorter than the calibration period. Daily precipitation data from two National Oceanic and Atmospheric Administration (NOAA) gages and hourly precipitation and other meteorological data for one NOAA gage were used for model input. The difference between simulated and observed streamflow volume ranged from -0.9 to 1.8 percent for the 4-year period at the two calibration sites with 4-year records. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 89.1 mi 2 ), annual differences between observed and simulated streamflow ranged from -5.8 to 14.4 percent and the overall error for the 4-year period was -0.9 percent. Calibration errors for 36 storm periods at the two calibration sites for total volume, low-flowrecession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the hourly rainfall data. The water-quality component of the model was calibrated using data collected by the USGS and state agencies at three USGS streamflow-measurement stations with variable water-quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for up to five storms in 1998 at each of the two nonpoint-source monitoring sites in the White Clay Creek Basin indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. The accuracy of the water-quality calibration under stormflow conditions is limited by the relatively small amount of water-quality data available for the White Clay Creek Basin. Users of the White Clay Creek HSPF model should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow and water quality for individual storm events may not be well simulated, but the model performance is reasonable when evaluated over longer periods of time; the observed flow-duration curve for the simulation period is similar to the long-term flow-duration curve at White Clay Creek near Newark, Del., indicating that the calibration period is representative of all but highest 0.1 percent and lowest 0.1 percent of flows at that site; relative errors in streamflow and water-quality simulations are greater for smaller drainage areas than for larger areas; and calibration for water-quality was based on sparse data.

Water-Resources Investigations Report↗

Effects of lakes and reservoirs on annual river nitrogen, phosphorus, and sediment export in agricultural and forested landscapes

Recently, effects of lakes and reservoirs on river nutrient export have been incorporated into landscape biogeochemical models. Because annual export varies with precipitation, there is a need to examine the biogeochemical role of lakes and reservoirs over time frames that incorporate interannual variability in precipitation. We examined long-term (~20&thinsp;years) time series of river export (annual mass yield, Y, and flow-weighted mean annual concentration, C) for total nitrogen (TN), total phosphorus (TP), and total suspended sediment (TSS) from 54 catchments in Wisconsin, USA. Catchments were classified as small agricultural, large agricultural, and forested by use of a cluster analysis, and these varied in lentic coverage (percentage of catchment lake or reservoir water that was connected to river network). Mean annual export and interannual variability (CV) of export (for both Y and C) were higher in agricultural catchments relative to forested catchments for TP, TN, and TSS. In both agricultural and forested settings, mean and maximum annual TN yields were lower in the presence of lakes and reservoirs, suggesting lentic denitrification or N burial. There was also evidence of long-term lentic TP and TSS retention, especially when viewed in terms of maximum annual yield, suggesting sedimentation during high loading years. Lentic catchments had lower interannual variability in export. For TP and TSS, interannual variability in mass yield was often >50% higher than interannual variability in water yield, whereas TN variability more closely followed water (discharge) variability. Our results indicate that long-term mass export through rivers depends on interacting terrestrial, aquatic, and meteorological factors in which the presence of lakes and reservoirs can reduce the magnitude of export, stabilize interannual variability in export, as well as introduce export time lags.

Wisconsin↗

The coral reef of South Moloka'i, Hawai'i— Portrait of a sediment-threatened fringing reef

Moloka‘i, with the most extensive coral reef in the main Hawaiian Islands, is especially sacred to Hina, the Goddess of the Moon. As Hinaalo, she is the Mother of the Hawaiian people; as Hinapuku‘a, she is the Goddess of Fishermen; and in the form Hina‘opuhalako‘a, she is the Goddess who gave birth to coral, coral reefs, and all spiny marine organisms. Interdependence between the reef’s living resources, the people, and their cosmology was the basis for management of Moloka‘i’s coastal waters for over a thousand years. The ancient residents of Moloka‘i built the greatest concentration of fishponds known anywhere, but their mastery of mariculture, something needed now more than ever, was lost after near genocide from exotic Western diseases. Subsequent destruction of the native vegetation for exotic cattle, goats, pigs, sugar cane, and pineapple caused soil erosion and sedimentation on the reef flat. This masterful volume clearly documents that soil washing into the sea is the major threat to the reef today. Abandoned fishponds, choked with sediment, now act as barriers and mud traps, making damage to corals less than it would otherwise would have been. The role of mud and freshwater from land in preventing coral reef growth, clearly articulated in Charles Darwin’s first book, The Structure and Distribution of Coral Reefs, is the major theme of this book. All around the tropics, coral reefs have died from huge increases in terrestrial sedimentation that resulted from destruction of hillside forests for cash-crop agriculture and pastures in the colonial era, especially in Latin America, Asia, and the islands of the Caribbean and Indo-Pacific. It is obvious that one cannot manage the coastal zone as a unit separate from the watersheds that drain into it. Yet there has been surprisingly little comprehensive scientific study of these impacts. In this landmark volume, U.S. Geological Survey researchers and their colleagues have developed and applied a remarkably integrated approach to the reefs of Moloka‘i, combining geology, oceanography, and biology to provide an in-depth understanding of the processes that have made these reefs grow and that now limit them. They have joined old fashioned natural history of marine animals and plants with study of the geological evolution of the island, hydrology, meteorology, and land-use history, to an arsenal of new methods of remote sensing, including aerial photography, laser ranging, infrared thermal mapping, seismic reflection, in-situ instrumentation to measure chemical parameters of water quality, and direct measurements of the physical driving forces affecting them—such as wave energy, currents, sedimentation, and sediment transport. They provide a level of documentation and insight that has never been available for any reef before. A remarkable feature of this book is that it is aimed at the people of Moloka‘i to inform them of what is happening to their reef and what they might do to preserve their vital resources. The scientific data and interpretations are expressed in unusually clear and comprehensible language, free of the professional jargon that makes most technical publications impenetrable to the public that most needs to know about them, yet without loss of scientific rigor. Here readers will see clearly explained the whole path of soil loss, from the impacts of wild pigs and goats at higher elevations, deforestation of the hills for cattle pasture at lower levels, and denudation of low lands for cash crops. The resulting biological impoverishment has bared the soils, which wash away in flash storms, smothering the inshore reefs, whose growth was already limited because they had grown right up to sea level. The data in this book show that the mud doesn’t get far if it is washed into the sea during a big storm with heavy waves. Afterwards this mud keeps getting stirred up by every succeeding storm, spreading and affecting corals over wider areas until it is finally washed out of the system—and that only happens if there is no more new mud washing onto the reef. I saw this myself a few years ago in Pila‘a Bay on Kaua‘i, where a bulldozed hillside of abandoned sugar cane fields had slumped right on top of a coral reef following exceptional rains. Years later, the algae species were zoned in a way that clearly mapped the distribution of nutrients washed into the bay, most likely from fertilizers bound to the eroded soils. That pattern closely mimics, on a small scale, that shown in Moloka‘i in this volume, where the inner reef is covered with algae, zoned by species in a way that points to land-based sources of nutrients, while the outermost reef slope is still coral dominated, and the deep algae seem to indicate deep-water nutrient upwelling. What of the future? The Hawaiian Islands have been exceptionally fortunate to be spared the worst coral heatstroke death from high temperatures, at least to date. So far, the worst global warming impacts have luckily been small in this region, and the small number of people on Moloka‘i has kept population densities, and sewage pollution, low compared to the more developed islands. Nutrients from years of sugar and pineapple fertilization, and the washing of this soil onto the reefs, show clear influences on the pattern of algae on the reef. Even at very low levels of nutrients, well below that which drives algae to smother and kill coral reefs, more algae is present. Soil erosion control is therefore the key to better management of both nutrients and turbidity on Moloka‘i reefs. To that end land management actions mentioned in this book, such as suppressing wild fires and eliminating wild goats and pigs, could be made even more effective if supplemented by active erosion control using plants whose roots bind the soil effectively in place. Through all of these efforts, Hina and the people of Moloka‘i could be happy again!

Hawaii↗

Shift of potential natural vegetation against global climate change under historical, current and future scenarios

Potential natural vegetation (PNV), the final successional stage of vegetation, plays a key role in ecological restoration, the design of nature reserves, and development of agriculture and livestock production. Meteorological data from historical and current periods including the last inter-glacial (LIG), last glacial maximum (LGM), mid Holocene (MH) periods and the present day (PD), plus derived data from 2050 and 2070, in conjunction with the Comprehensive and Sequential Classification System (CSCS) model, were used to classify global PNV. The 42 classes of global PNV were regrouped into 10 groups to facilitate analysis of spatial changes. Finally, spatio-temporal patterns and successional processes of global PNV as well as the response to climate changes were analysed. Our study made the following five conclusions. (1) Only one missing class (IA1 frigid-extrarid frigid desert, alpine desert) arose in periods of LIG, MH, 2050, and 2070 for global PNV. (2) The frigid-arid groups were mainly distributed in higher latitudes and elevations, but temperate-humid groups and tropical-perhumid groups occurred in middle and low latitudes, respectively. Temperate zonal forest steppe, warm desert, savanna and tropical zonal forest steppe increased, while six other groups decreased. (3) The conversion from temperate zonal forest steppe to tundra and alpine steppe from LIG to LGM occupied the largest area, indicating a drastic shift in climate and the associated response of terrestrial vegetation sensitive to climate change. (4) The CSCS could be used to simulate the long-term succession of global PNV. (5) As a consequence of global warming, forests shifted to the northern hemisphere and Tibet, areas with much higher latitude and elevation. The PNV groups with greater shift distance revealed the more serious effects of global climate change on vegetation.

Rangeland Journal↗