BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Watershed Evapotranspiration Increased due to Changes in Vegetation Composition and Structure Under a Subtropical Climate1

Abstract: Natural forests in southern China have been severely logged due to high human demand for timber, food, and fuels during the past century, but are recovering in the past decade. The objective of this study was to investigate how vegetation cover changes in composition and structure affected the water budgets of a 9.6‐km² Dakeng watershed located in a humid subtropical mountainous region in southern China. We analyzed 27 years (i.e., 1967‐1993) of streamflow and climate data and associated vegetation cover change in the watershed. Land use/land cover census and Normalized Difference of Vegetation Index (NDVI) data derived from remote sensing were used to construct historic land cover change patterns. We found that over the period of record, annual streamflow (Q) and runoff/precipitation ratio did not change significantly, nor did the climatic variables, including air temperature, Hamon’s potential evapotranspiration (ET), pan evaporation, sunshine hours, and radiation. However, annual ET estimated as the differences between P and Q showed a statistically significant increasing trend. Overall, the NDVI of the watershed had a significant increasing trend in the peak spring growing season. This study concluded that watershed ecosystem ET increased as the vegetation cover shifted from low stock forests to shrub and grasslands that had higher ET rates. A conceptual model was developed for the study watershed to describe the vegetation cover‐streamflow relationships during a 50‐year time frame. This paper highlighted the importance of eco‐physiologically based studies in understanding transitory, nonstationary effects of deforestation or forestation on watershed water balances.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.     

潮白河流域1980—2013年平均水平衡特征研究

论文采用潮河、白河流域1980—2013年间气象、水文资料,基于水热耦合模型方法,分析了潮河、白河流域水循环要素相应于不同土地覆被类型结构变化和水平衡的特征。通过对未来10 a流域下垫面状况的预估,预测了未来变化环境条件下流域径流量的变化情况。研究对预测潮白河流域水资源变化特征、确保密云水库水资源安全具有重要意义。研究结果显示:该模型在潮、白河流域适用;在现有下垫面资料的基础上,利用林地面积和草地流域下垫面参数相结合的方法,对潮河流域和白河流域未来下垫面的变化分别做出了11种预测情景,并在11种情景的基础上预测两流域的未来10 a径流变化,结果显示潮河流域的径流深在26.47~53.55mm范围内波动,而白河流域的径流深在17.57~41.53 mm范围内变动。研究的创新点为,在对未来下垫面状况预测的基础上,利用水热耦合模型预测流域未来的可能径流状况。     

RELATION OF NITRATE CONCENTRATIONS TO BASEFLOW IN THE RACCOON RIVER,IOWA1

ABSTRACT: Excessive nitrate‐nitrogen (nitrate) export from the Raccoon River in west central Iowa is an environmental concern to downstream receptors. The 1972 to 2000 record of daily streamflow and the results from 981 nitrate measurements were examined to describe the relation of nitrate to streamflow in the Raccoon River. No long term trends in streamflow and nitrate concentrations were noted in the 28‐year record. Strong seasonal patterns were evident in nitrate concentrations, with higher concentrations occurring in spring and fall. Nitrate concentrations were linearly related to streamflow at daily, monthly, seasonal, and annual time scales. At all time scales evaluated, the relation was improved when baseflow was used as the discharge variable instead of total streamflow. Nitrate concentrations were found to be highly stratified according to flow, but there was little relation of nitrate to streamflow within each flow range. Simple linear regression models developed to predict monthly mean nitrate concentrations explained as much as 76 percent of the variability in the monthly nitrate concentration data for 2001. Extrapolation of current nitrate baseflow relations to historical conditions in the Raccoon River revealed that increasing baseflow over the 20th century could account for a measurable increase in nitrate concentrations.     

SUMMER LOW FLOWS IN NEW ENGLAND DURING THE 20TH CENTURY1

ABSTRACT: High springtime river flows came earlier by one to two weeks in large parts of northern New England during the 20th Century. In this study it was hypothesized that late spring/early summer recessional flows and late summer/early fall low flows could also be occurring earlier. This could result in a longer period of low flow recession and a decrease in the magnitude of low flows. To test this hypothesis, variations over time in the magnitude and timing of low flows were analyzed. To help understand the relation between low flows and climatic variables in New England, low flows were correlated with air temperatures and precipitation. Analysis of data from 23 rural, unregulated rivers across New England indicated little evidence of consistent changes in the timing or magnitude of late summer/early fall low flows during the 20th Century. The interannual variability in the timing and magnitude of the low flows in northern New England was explained much more by the interannual variability in precipitation than by the interannual variability of air temperatures. The highest correlation between the magnitude of the low flows and air temperatures was with May through November temperatures (r =?0.37, p= 0.0017), while the highest correlation with precipitation was with July through August precipitation (r = 0.67, p > 0.0001).     

MANAGEMENT OF INSTREAM FLOWS THROUGH RUNOFF DETENTION AND RETENTION1

ABSTRACT: The use of reservoirs and land treatments to manage streamflow for the maintenance or enhancement of instream flow values is a valid concept. Historically, large reservoirs have been used for flood control and water-supply regulation. Smaller structures have enjoyed widespread use for soil and water conservation in headwater areas. Where reservoir releases can be controlled, it is technically feasible to regulate flows for the enhancement of instream values. However, institutional and political obstacles may preclude the operation of some reservoirs for this purpose. Retention and detention structures and land treatments, implemented for soil and water conservation purposes, have often had favorable effects on the streamflow hydrograph. Decreases in peak flows and increases in low flows have been documented. Design concepts for runoff-control structures are discussed in relation to instream flow management objectives. Hydro-logic simulation is offered as a potential tool for project design and feasibility analysis.     

IS IT REALISTIC TO DEFINE A 100-YEAR DROUGHT FOR WATER MANAGEMENT?1

ABSTRACT: The concept of recurrence interval has been used for years in engineering designs. Can the same concept be applied to the drought analysis? This paper uses the plotting position method to define drought of various recurrence intervals based on stream-flow data. The method of truncation level was applied to the same data to examine the defined drought. Based on the method of truncation level, drought duration and its corresponding flow deficit were investigated. Eighteen flow gage stations from the Scioto River Basin in Ohio were selected for the study. The results show that flows of 100-year droughts using the plotting position method are practically nil. On the other hand, flows of droughts using the truncation method are gradually decreasing with an increase in truncation level, where flows of 95 percent are approximately equal to those of two-year droughts defined by the plotting position. It is also shown that there is a strung correlation between drought duration and deficit.     

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.