Search

Summary Distributed Hydrology Soil Vegetation Model (DHSVM) was successfully applied in Mākaha watershed to investigate the hydrologic impact of groundwater pumping on the streamflow. DHSVM was calibrated (1971–1980) and validated (1981–1990) against observed streamflow during pre-development conditions. Monthly observed and simulated streamflows after the development (after 1990) were compared to quantify the decline in streamflow. Simulation results indicate that declines in streamflow could be attributed to: (i) an increase in the fraction of gross rainfall converted into evapotranspiration (ET); and (ii) groundwater pumping. Groundwater pumping from additional groundwater development in the form of new pumping wells starting in 1991 has significantly reduced the streamflow, especially during the summer. The fraction of gross rainfall converted into ET has significantly increased by 7% during 1991–2009 compared to the pre-development period. Based on the model results, in the absence of the groundwater development in the upper part of Mākaha watershed, the observed cumulative streamflow could have been 11% higher than its current level. On an annual basis, total additional groundwater pumping accounts for 26% of the recharge with a significant seasonal variability (12% in wet and 180% in dry season). Relative to recharge, higher groundwater pumping during the dry season could have been the reason behind the significant streamflow decline during the summer. This study clearly emphasizes the importance of temporal patterns in groundwater withdrawal which are often ignored while determining safe yield.

Daily and sub-daily weather data are often required for hydrological and environmental modeling. Various weather generator programs have been used to generate synthetic climate data where observed climate data are limited. In this study, a weather data generator, ClimGen, was evaluated for generating information on daily precipitation, temperature, and wind speed at four tropical watersheds located in Hawai‘i, USA. We also evaluated different daily to sub-daily weather data disaggregation methods for precipitation, air temperature, dew point temperature, and wind speed at Mākaha watershed. The hydrologic significance values of the different disaggregation methods were evaluated using Distributed Hydrology Soil Vegetation Model. MuDRain and diurnal method performed well over uniform distribution in disaggregating daily precipitation. However, the diurnal method is more consistent if accurate estimates of hourly precipitation intensities are desired. All of the air temperature disaggregation methods performed reasonably well, but goodness-of-fit statistics were slightly better for sine curve model with 2 h lag. Cosine model performed better than random model in disaggregating daily wind speed. The largest differences in annual water balance were related to wind speed followed by precipitation and dew point temperature. Simulated hourly streamflow, evapotranspiration, and groundwater recharge were less sensitive to the method of disaggregating daily air temperature. ClimGen performed well in generating the minimum and maximum temperature and wind speed. However, for precipitation, it clearly underestimated the number of extreme rainfall events with an intensity of >100 mm/day in all four locations. ClimGen was unable to replicate the distribution of observed precipitation at three locations (Honolulu, Kahului, and Hilo). ClimGen was able to reproduce the distributions of observed minimum temperature at Kahului and wind speed at Kahului and Hilo. Although the weather data generation and disaggregation methods were concentrated in a few Hawaiian watersheds, the results presented can be used to similar mountainous location settings, as well as any specific locations aimed at furthering the site-specific performance evaluation of these tested models.

Paddy is commonly grown under flooded or submerged condition in which substantial amount of water is lost by different processes. Puddling is traditionally done to reduce water loss from lowland rice fields. Since the very step of puddling, rice root zone undergoes structural changes leading to the formation of a layered profile having a hydraulically less-conductive plow sole below the root zone. However, studies have shown that soil cracking and the presence of preferential flow paths in puddle fields defeat this purpose. Description of soil water regime in such a dynamic soil requires an in situ measurement method for soil hydraulic properties. A field experiment was conducted in twelve 30 m2 plots during the rainy seasons (June to October) of 2004 and 2005 to evaluate a suitable method for estimating soil hydraulic properties of lowland paddy soil. Results showed that piezometric (pressure) heads installed in different soil layers responded to the drying and wetting cycles typically followed in transplanted rice and are observed as a part of monsoon climate in eastern India. The Marquardt-Levenberg algorithm built in the HYDRUS-1D simulation environment was used to inversely estimate soil hydraulic parameters. Estimated parameters revealed larger hydraulic conductivity for the compacted plow sole than those published in literature, which may have resulted from alternate wetting and drying typically observed under monsoon climate and earthworm burrows observed in our experimental field. Results from simulation studies suggest that both the single- and dual-porosity models could simulate water flow considerably well in lowland paddy field although the latter described pressure head time series data slightly better in about 50% of simulations. Similar performance of the single- vs. dual-porosity model may have resulted from estimating a seasonally mean soil hydraulic properties which include the effect of both preferential flow and matrix flow as the specific soil and boundary conditions prevailed. While water may have preferentially transported through the macropores during the wetting cycles in a near-saturated soil, it would have dominantly moved through soil matrix during the drying cycles. This study shows that simple piezometers may be combined with a simulation model to estimate hydraulic properties of different soil layers in a lowland paddy field.

Several newly developed capacitance sensors have simplified real-time determination of soil water content. Previous work has shown that salinity and temperature can affect these sensors, but relatively little has been done to correct these effects. The objectives of this study were to evaluate the effect of media temperature and salinity on the apparent water content measured with a single capacitance sensor (SCS), and to mitigate this effect using a temperature dependent scaled voltage technique under laboratory conditions. A column study was conducted containing two media: pure deionized water and quartz sand under varying water contents (0.05 to 0.30 cm 3 cm −3 ) and salinity (0 to 80 mmol L −1 ). Media temperature was varied between 5 and 45 °C using an incubator. The SCS probes and thermocouples were placed in the middle of the columns and were logged at an interval of 1 minute. There was strong negative correlation between sensor reading and temperature of deionized water with a rate of −0.779 mV °C −1 . Rates of SCS apparent output were 0.454 and 0.535 mV °C −1 for air in heating and cooling cycles, respectively. A similar positive correlation with temperature was observed in sand at different water contents. The SCS probe was less sensitive to temperature as salinity and water content increased. Using a temperature-corrected voltage calibration model, the effect of temperature was reduced by 98%. An analytical model for salinity correction was able to minimize the error as low as ± 2% over the salinity level tested.

Manure amendments affect several soil physical and hydrologic properties. The objectives of this study were to evaluate the effect of (i) manure amendment rates (0, 168, 336, and 672 kg total N ha -1 ), levels (one-time application, and two-time application), and types (chicken manure [CM], dairy manure [DM], and swine manure [SM]) on bulk density (ρ b ), total soil porosity (θ t ), and saturated hydraulic conductivity (K sat ) of a highly weathered tropical soil (Waialua gravely clay variant, isohyperthermic Pachic Haplustolls) and (ii) measuring instruments (tension infiltrometer [TI] and double-ring infiltrometers [DR]) on K sat . For the two-time application level, all the plots received additional manure to compensate for the amounts decomposed during the first growing season. The field was tilled to the top 15-cm depth before and after manure application during the two growing seasons. Measurements of ρ b and θ t were conducted on undisturbed soil core samples collected from the top 10 cm of soil from a field cultivated with sweet corn (Zea mays L. subsp. mays) and irrigated with a drip irrigation system for two consecutive growing seasons. Saturated hydraulic conductivity was calculated from steady state infiltration rates measured with TI and DR. Results show that the increased manure amendment rates and levels significantly (P < 0.01) decreased ρ b and consequently increased θ t . The values of K sat increased significantly (P < 0.01) with increase in CM and DM amendment rates and levels. For SM treatments, the values of K sat decreased with increase in manure amendment rates and levels; these results concur with those reported in literature on liquid manures. Saturated hydraulic conductivity calculated from DR data was slightly greater than that from TI data for CM and DM treatments. Opposite results were found for SM treatments. However, a good agreement (R2 = 0.90) was observed between K sat values calculated from TI and DR data across treatments. We conclude that the CM and DM amendments increased θ t and K sat and may also temporarily improve soil aggregation; however, SM amendments result in decreased K sat . Thus, it is not recommended that slurry SM be applied to the soils with low hydraulic conductivity because it could further inhibit water infiltration and increase surface runoff and soil erosion.

Land use and climate change occur simultaneously around the globe. Fully understanding their separate and combined effects requires a mechanistic understanding at the local scale where their effects are ultimately realized. Here we applied an individual-based model of fish population dynamics to evaluate the role of local stream variability in modifying responses of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) to scenarios simulating identical changes in temperature and stream flows linked to forest harvest, climate change, and their combined effects over six decades. We parameterized the model for four neighboring streams located in a forested headwater catchment in northwestern Oregon, USA with multi-year, daily measurements of stream temperature, flow, and turbidity (2007–2011), and field measurements of both instream habitat structure and three years of annual trout population estimates. Model simulations revealed that variability in habitat conditions among streams (depth, available habitat) mediated the effects of forest harvest and climate change. Net effects for most simulated trout responses were different from or less than the sum of their separate scenarios. In some cases, forest harvest countered the effects of climate change through increased summer flow. Climate change most strongly influenced trout (earlier fry emergence, reductions in biomass of older trout, increased biomass of young-of-year), but these changes did not consistently translate into reductions in biomass over time. Forest harvest, in contrast, produced fewer and less consistent responses in trout. Earlier fry emergence driven by climate change was the most consistent simulated response, whereas survival, growth, and biomass were inconsistent. Overall our findings indicate a host of local processes can strongly influence how populations respond to broad scale effects of land use and climate change.

California has recently experienced one of the worst droughts on record, negatively impacting forest ecosystems across the state. As a major source of the region’s water supply, it is important to evaluate the vegetation and water balance response of these montane forested watersheds to climate variability across the range of rain- to snow-dominated precipitation regimes. The Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) were used to capture the hydrologic drought signal, and MODIS vegetation indices (i.e., the normalized difference vegetation index and the enhanced vegetation index) were used to evaluate the vegetation and evapotranspiration response in three headwater catchments. The study catchments comprised a low elevation rain-dominated site (Caspar Creek) on the northern California coast, a mid-elevation site with a mix of rain and snow (Providence Creek) in the California Sierra Nevada, and a high elevation snow-dominated site (Bull Creek) in the Sierra Nevada. Lowest SPI values occurred in the third drought year of 2014 for all sites. Lowest SRI was in 2014 for Caspar, but in 2015 for Providence and Bull, reflecting differences in snowpack-delayed runoff and subsurface storage capacity between the lower and higher elevation watersheds. The most accurate water balance closure using evapotranspiration estimates from vegetation indices was within 10% of measured precipitation at snow-dominated Bull. The rain-dominated Caspar watershed had the highest vegetation index values and annual evapotranspiration, with the lowest variability over the previous 13 years (2004–2016). Vegetation index values and annual evapotranspiration decreased with increasing elevation and snow contribution to precipitation. Both snow-influenced Sierra Nevada watersheds showed elevated vegetation and evapotranspiration responses to interannual climate variability. There remains a need for institutional support to expand long-term observations in remote forested mountain watersheds to monitor and research these changing and extreme environmental conditions in source watershed regions.

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11–44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature–stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 °C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time. S Online supplementary data available from stacks.iop.org/ERL/9/084015/mmedia