Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R², root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R² and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R² and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R² increase of only 0.08. Similarly, improvement in R² was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.     

ESTIMATING STREAMFLOW AND ASSOCIATED HYDRAULIC GEOMETRY,THE MID‐ATLANTIC REGION,USA1

ABSTRACT: Methods to estimate streamflow and channel hydraulic geometry were developed for unpaged streams in the Mid‐Atlantic Region. Observed mean annual streamflow and associated hydraulic geometry data from 75 gaging stations in the Appalachian Plateau, the Ridge and Valley, and the Piedmont Physiographic Provinces of the Mid‐Atlantic Region were used to develop a set of power functions that relate streamflow to drainage area and hydraulic geometry to streamflow. For all three physiographic provinces, drainage area explained 95 to 98 percent of the variance in mean annual streamflow. Relationships between mean annual streamflow and water surface width and mean flow depth had coefficients of determination that ranged from R²= 0.55 to R²= 0.91, but the coefficient of determination between mean flow velocity and mean annual streamflow was lower (R²= 0.44 to R²= 0.54). The advantages of using the regional regression models to estimate streamflow over a conceptual model or a water balance model are its ease of application and reduced input data needs. The prediction of the regression equations were tested with data collected as part of the U.S. Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). In addition, equations to transfer streamflow from gaged to ungaged streams are presented.     

Air‐Water Temperature Relationships in the Trout Streams of Southeastern Minnesota's Carbonate‐Sandstone Landscape

Carbonate‐sandstone geology in southeastern Minnesota creates a heterogeneous landscape of springs, seeps, and sinkholes that supply groundwater into streams. Air temperatures are effective predictors of water temperature in surface‐water dominated streams. However, no published work investigates the relationship between air and water temperatures in groundwater‐fed streams (GWFS) across watersheds. We used simple linear regressions to examine weekly air‐water temperature relationships for 40 GWFS in southeastern Minnesota. A 40‐stream, composite linear regression model has a slope of 0.38, an intercept of 6.63, and R² of 0.83. The regression models for GWFS have lower slopes and higher intercepts in comparison to surface‐water dominated streams. Regression models for streams with high R² values offer promise for use as predictive tools for future climate conditions. Climate change is expected to alter the thermal regime of groundwater‐fed systems, but will do so at a slower rate than surface‐water dominated systems. A regression model of intercept vs. slope can be used to identify streams for which water temperatures are more meteorologically than groundwater controlled, and thus more vulnerable to climate change. Such relationships can be used to guide restoration vs. management strategies to protect trout streams.     

NUTRIENT AND β17‐ESTRADIOL LOSS IN RUNOFF WATER FROM POULTRY LITTERS1

ABSTRACT: A main water quality concern is accelerated eutrophication of fresh waters from nonpoint source pollution, particularly nutrient transport in surface runoff from agricultural areas and confined animal feeding operations. This study examined nutrient and β₁₇‐estradiol concentrations in runoff from small plots where six poultry litters were applied at a rate of about 67 kg/ha of total phosphorus (TP). The six poultry litter treatments included pelleted compost, pelleted litter, raw litter, alum (treated) litter, pelleted alum litter, and normal litter (no alum). Four replicates of the six poultry litter treatments and a control (plots without poultry litter application) were used in this study. Rainfall simulations at intensity of 50 mm/hr were conducted immediately following poultry litter application to the plots and again 30 days later. Composite runoff samples were analyzed for soluble reactive phosphorus (SRP), ammonia (NH₄), nitrate (NO₃), TP, total nitrogen (TN) and β₁₇‐estradiol concentrations. In general, poultry litter applications increased nutrient and β₁₇‐estradiol concentrations in runoff water. Ammonia and P concentrations in runoff water from the first simulation were correlated to application rates of water extractable NH₄ (R²= 0.70) and P (R²= 0.68) in the manure. Results suggest that alum applications to poultry litter in houses in between flocks is an effective best management practice for reducing phosphorus (P) and β₁₇‐estradiol concentrations in runoff and that pelleted poultry litters may increase the potential for P and β₁₇‐estradiol loss in runoff water. Inferences regarding pelleted poultry litters should be viewed cautiously, because the environmental consequence of pelleting poultry litters needs additional investigation.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

Characterizing Runoff and Water Yield for Headwater Catchments in the Southern Sierra Nevada

In a Mediterranean climate where much of the precipitation falls during winter, snowpacks serve as the primary source of dry season runoff. Increased warming has led to significant changes in hydrology of the western United States. An important question in this context is how to best manage forested catchments for water and other ecosystem services? Answering this basic question requires detailed understanding of hydrologic functioning of these catchments. Here, we depict the differences in hydrologic response of 10 catchments. Size of the study catchments ranges from 50 to 475 ha, and they span between 1,782 and 2,373 m elevation in the rain‐snow transitional zone. Mean annual streamflow ranged from 281 to 408 mm in the low elevation Providence and 436 to 656 mm in the high elevation Bull catchments, resulting in a 49 mm streamflow increase per 100 m (R² = 0.79) elevation gain, despite similar precipitation across the 10 catchments. Although high elevation Bull catchments received significantly more precipitation as snow and thus experienced a delayed melt, this increase in streamflow with elevation was mainly due to a reduction in evapotranspiration (ET) with elevation (45 mm/100 m, R² = 0.65). The reduction in ET was attributed to decline in vegetation density, growing season, and atmospheric demand with increasing elevation. These findings suggest changes in streamflow in response to climate warming may likely depend on how vegetation responds to those changes in climate.     

Small farm ponds: overlooked features with important impacts on watershed sediment transport

Despite their size, small farm ponds are important features in many landscapes. Yet hydrographical databases often fail to capture these ponds, and their impacts on watershed processes remain unclear. For a 230‐km² portion of central Texas, United States (U.S.), we created a historical inventory of ponds and quantified the accuracy of automated detection methods under varying drought conditions. In addition, we documented pond dredging/enlargement events and identified sites that had been abandoned. We also analyzed sediment cores from downstream reservoirs to track changes in watershed sediment transport. Over 75 years, pond densities increased more than 350% — to among the highest documented in the U.S. — and the ability of automated methods to detect these ponds was highly dependent on drought severity (R² = 0.96). Approximately 5% of ponds present in the 1950s were no longer present in 2012, while 33% were dredged between 1937 and 2012. Downstream reservoir sedimentation has decreased by an average of 55% as ponds have increased in number. These findings suggest that small ponds and the maintenance of trapping efficiency have large‐scale impacts on sediment dynamics. Accurately accounting for these storage effects is vital to water resource planning efforts.     

Climate and Water Conflicts Coevolution from Tropical Development and Hydro‐Climatic Perspectives: A Case Study of Costa Rica

Costa Rica is a nation with a vast wealth of water resources; however, recently the country has faced water conflicts (WC) due to social, economic, legal, and political impediments in response to limited water availability during El Niño events and inefficient use of its water resources. This study presents a spatial distribution and temporal analysis of WC in Costa Rica from 2005 to 2015. In total, 719 WC were analyzed of which 54% were among private individuals and government. The largest urban areas and the Grande de Tárcoles Basin were identified as the main “hot spot” for the conflicts. WC were mainly caused by spills of wastewater, water pollution, water shortage, infrastructure damage, and flooding, and can be predicted using a multiple linear model including the population size and the number of hydro‐meteorological events (HME) (R² = 0.77). The identified HME also coevolved significantly with the changes in precipitation regimes (r = 0.67, p = 0.021). Our results suggest that there is a need to recognize that water infrastructure longevity across the country concatenates and amplifies WC, mainly in the most populated area located in the Central Valley. Implications of our findings include the need for truly integrated water resources management plans that include, for example, WC as indicators of hydro‐climatic changing conditions and water supply and sanitation infrastructure status.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Performance of the National Water Model in Iowa Using Independent Observations

This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (r_s), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km²) basins (r_s = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km²) basins (r_s = 0.6, E = ?0.25, KGE = ?0.2). Analysis with independent IFCBS observations was used to report performance at large basins (r_s = 0.6, KGE = 0.1) and small basins (r_s = 0.2, KGE = ?0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

STREAM WATER CHEMISTRY IN WATERSHEDS RECEIVING DIFFERENT ATMOSPHERIC INPUTS OF H+, NH4, NO3−, AND SO42−1

ABSTRACT: Weekly precipitation and stream water samples were collected from small watersheds in Denali National Park, Alaska, the Fraser Experimental Forest, Colorado, Isle Royale National Park, Michigan, and the Calumet watershed on the south shore of Lake Superior, Michigan. The objective was to determine if stream water chemistry at the mouth and upstream stations reflected precipitation chemistry across a range of atmospheric inputs of H⁺, NH₄⁺, NO₃^?_?, and SO₄^2?. Volume-weighted precipitation H⁺, NH₄⁺, NO₃^?_?, and SO₄^2? concentrations varied 4 to 8 fold with concentrations highest at Calumet and lowest in Denali. Stream water chemistry varied among sites, but did not reflect precipitation chemistry. The Denali watershed, Rock Creek, had the lowest precipitation NO₃^?_? and SO₄^2? concentrations, but the highest stream water NO₃^?and SO₄^2? concentrations. Among sites, the ratio of mean monthly upstream NO₃^?_? concentration to precipitation NO₃^?_- concentration declined (p < 0.001, R²= 0.47) as precipitation NO₃^?_? concentration increased. The ratio of mean monthly upstream to precipitation SO₄^2? concentration showed no significant relationship to change in precipitation SO₄^2? concentration. Watersheds showed strong retention of inorganic N (> 90 percent inputs) across inputs ranging from 0.12 to > 6 kg N ha^?1 y^?1. Factors possibly accounting for the weak or non-existent signal between stream water and precipitation ion concentrations include rapid modification of meltwater and precipitation chemistry by soil processes, and the presence of unfrozen soils which permits winter mineralization and nitrification to occur.     

Long‐Term High‐Resolution Radar Rainfall Fields for Urban Hydrology

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km²) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km², suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km²) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.     

Calculating Field‐Scale Evapotranspiration with Closed‐Chamber and Remote Sensing Methods

Currently, there is no agreed upon method for estimating evapotranspiration (ET) across large regions such as the state of New Mexico. Remote sensing methods have potential for providing a solution, but require validation. A comparison between field‐scale ET measurements using a portable chamber ET measurement device and modeled ET using the remote sensing Regional Evapotranspiration Estimation Model (REEM) was performed where the model had not been previously evaluated. Data were collected during the growing season of 2015 in three irrigated agricultural valleys of northern New Mexico in agricultural and nonagricultural settings. No statistically significant difference was observed in agricultural datasets between means of measured (M = 3.7 mm/day, SE = 0.31 mm/day) and modeled (M = 4.0 mm/day, SE = 0.01 mm/day) daily ET; t(17) = ?1.50, p = 0.15, α = 0.05. As there was no statistical difference observed between agricultural datasets, results support the use of REEM in irrigated agricultural areas of northern New Mexico. A statistically significant difference was observed in nonagricultural datasets between means of measured (M = 1.7 mm/day, SE = 0.22 mm/day) and modeled (M = 0.0 mm/day, SE = 0.00 mm/day) daily ET; t(9) = 1.79, p = 5.7 × 10^?6, α = 0.05. With additional calibrations and air temperature sensors placed outside of agricultural areas, REEM may be suitable for use in nonagricultural areas of northern New Mexico.     

Hydrobiogeochemical Controls on Riparian Nutrient and Greenhouse Gas Dynamics: 10 Years Post‐Restoration

Little is known about the impact of agricultural legacy on subsurface biogeochemical processes in the years following restoration of riparian wetlands (WLs). More knowledge is also needed on the relative importance of seasons, precipitation events, and inputs of water and nutrients driving nitrogen (N), phosphorus (P), sulfur (S), and greenhouse gas (GHG) (N₂O, CO₂, CH₄) dynamics in these systems. This investigation of a riparian zone comprising a restored WL area and a nonrestored well‐drained alluvium (AL) area in the United States Midwest revealed that despite successful hydrological restoration a decade earlier, biogeochemical conditions in the WL area remained less anoxic than in natural WLs, and not significantly different from those in the AL area. No significant differences in N, P, S, and C compound concentrations or fluxes were observed between the AL and WL areas. Over the duration of the study, nitrate (NO₃^?) and soluble reactive phosphorus appeared to be primarily driven by hillslope contributions. Ammonium (NH₄⁺), sulfate (SO₄^2?), and CO₂ responded strongly to seasonal changes in biogeochemical conditions in the riparian zone, while N₂O and CH₄ fluxes were most influenced by large rewetting events. Overall, our results challenge overly simplistic assumptions derived from direct interpretation of redox thermodynamics, and show complex patterns of solutes and GHGs at the riparian zone scale.     

Modeling Discharge Rates Using a Coupled Modeled Approach for the Merced River in Yosemite National Park

This study describes the application of the NASA version of the Carnegie‐Ames‐Stanford Approach (CASA) ecosystem model coupled with a surface hydrologic routing scheme previously called the Hydrological Routing Algorithm (HYDRA) to model monthly discharge rates from 2000 to 2007 on the Merced River drainage in Yosemite National Park, California. To assess CASA‐HYDRA's capability to estimate actual water flows in extreme precipitation years, the focus of this study is the 2007 water year, which was very dry, and the 2005 water year, which was a moderately wet year in the historical record. Prior to comparisons to gauge records, CASA‐HYDRA snowmelt algorithms were modified with equations from the U.S. Department of Agriculture Snowmelt‐Runoff Model (SRM), which has been designed to predict daily streamflow in mountain basins where snowmelt is a major runoff factor. Results show that model predictions closely matched monthly flow rates at the Pohono Bridge gauge station (USGS#11266500), with R² = 0.67 and Nash‐Sutcliffe (E) = 0.65. By subdividing the upper Merced River basin into subbasins with high spatial resolution in the gridded modeling approach, we were able to determine which biophysical characteristics in the Sierra differed to the largest degree in extreme low‐flow and high‐flow years. Average elevation and snowpack accumulation were found to be the most important explanatory variables to understand subbasin contributions to monthly discharge rates.     

PRISM vs. CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models

Precipitation is one of the most important drivers in watershed models. Our objective was to compare two sources of interpolated precipitation data in terms of their effect on calibration and validation of two Soil and Water Assessment Tool (SWAT) models. One model was a suburban watershed in metropolitan Atlanta, Georgia. The precipitation sources were Parameter‐elevation Relationships on Independent Slopes Model (PRISM) data on a 4‐km grid and climate forecast system reanalysis (CFSR) data on a 38‐km grid. The PRISM data resulted in a better fit to the calibration data (Nash Sutcliffe efficiency [NSE] = 0.64, Kling‐Gupta efficiency [KGE] = 0.74, p‐factor = 0.84, and r‐factor = 0.43) than the CFSR data (NSE = 0.47, KGE = 0.53, p‐factor = 0.67, and r‐factor = 0.39). Validation results were similar. Sensitive parameters were similar in both the PRISM and CFSR models, but fitted values indicated more rapid groundwater flow to the streams with the PRISM data. The same comparison was made in the Big Creek watershed located approximately 1,000 km away, in central Louisiana. Results were similar with a more responsive groundwater system indicating PRISM data may produce better predictions of streamflow because of a more accurate estimate of rainfall within a watershed or because of a denser grid. Our study implies PRISM is providing a better estimate than CFSR of precipitation within a watershed when rain gauge data are not available, resulting in more accurate simulations of streamflows at the watershed outlet. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Evapotranspiration in the Meso‐Scale Forested Watersheds in Minjiang Valley,West China1

Abstract: Information on evapotranspiration (ET) can help us understand water balance, particularly in forested watersheds. Previous studies in China show that ET was relatively low (30‐40% of total precipitation) in the Minjiang Valley located in the upper reach of the Yangtze River Basin. However, this conclusion was derived from research on small‐scale watersheds (<100 km²). The objective of this paper was to present ET information on meso‐scale watersheds in the Minjiang Valley. Four meso‐scale watersheds (1,700‐5,600 km²) located in the Minjiang Valley were used to estimate ET using the water balance approach. We first generated forest vegetation variables (coniferous forest percentage, forest cover percentage, and derived forest vegetation index) using remote sensing data. Landsat 5 TM satellite images, acquired on June 26, 1994, were selected for the vegetation classification. Actual annual ET was calculated based on 11‐year estimated precipitation and measured streamflow data (1992‐2002). We also calculated potential ET (PET) using an improved Thornthwaite model for all four watersheds for the period of 1992‐1998. PET can provide additional information about potential capacity of water flux to atmosphere in the region. Seasonal (dry and rainy) PET and ET for all studied watersheds were also estimated for comparison purposes as the water balance approach, at shorter than annual scales, would likely provide inaccurate estimates of ET. The dominant vegetations in the Minjiang Valley were grasslands, conifer forests, and shrub‐lands. Our results confirmed that both ET and PET for three studied meso‐scale watersheds in the Minjiang Valley is relatively low (39.5‐43.8 and 28.2‐47.7% for ET and PET, respectively), with an exception of ET in the Yuzixi watershed being 71.1%. This result is generally consistent with previous research at small watershed scales. Furthermore, the low ET across various scales in the Minjiang Valley may be related to the unique deeply cut valley environment.