Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations

This article is an assessment of the current state of the art and relative utility of satellite precipitation products (SPPs) for hydrologic applications to support water management decisions. We present a review of SPPs, their accuracy in diverse settings including the influence of geography, topography, and weather systems, as well as the pros and cons of their use for different water management applications. At the end of this broad synthesizing effort, recommendations are proposed for: (1) SPP developers to improve the quality, usability, and relevance of precipitation products; and (2) SPP users to improve the reliability of their predictions and hydrologic applications to better support water management.     

Upstream Sediment‐Control Dams: Five Decades of Experience in the Rapidly Eroding Dahan River Basin,Taiwan

Sedimentation is emerging as a key issue in sustainable reservoir management. One approach to controlling reservoir sedimentation is to trap sediment in hydraulic structures upstream of the reservoir. In the 1,163‐km² catchment of the Dahan River (Taiwan) over 120 “sabo” dams were built to reduce sediment yield to Shihmen Reservoir. Built in 1963 for water supply, Shihmen has lost over 40% of its 290‐Mm³ storage capacity to sedimentation. Most of these upstream structures were small, but three had capacities >9 Mm³. Field measurements and historical data from the Water Resources Agency show most smaller dams had filled with sediment by 1976. The three largest were full or nearly so by 2007, when one (Barlin Dam) failed, releasing a pulse of 7.5 Mm³, most of its 10.4 Mm³ stored sediment downstream. The Central Range of Taiwan is rapidly eroding (denudation rates 3‐6 mm/yr), so geologically high loads make sediment problems manifest sooner. Even in other environments, however, eventually small dams built upstream of large reservoirs are likely to fill themselves, creating multiple small sediment‐filled reservoirs, some located in sites inaccessible to mechanical removal. Our analysis suggests sabo dams do not offer a long‐term basis for controlling reservoir sedimentation in such a high‐sediment yield environment. Sustainable solutions must somehow pass sediment downstream, as would be accomplished by a sediment bypass around Shihmen Reservoir, as now being studied.     

Factors Affecting Flood Disasters in Small Island Developing States and Potential Adaptation Measures: The Case of Funchal City,Portugal

This work highlights the evolution of flood disasters and their impacts upon environmental and socioeconomic aspects both worldwide and in Small Island Developing States (SIDS) in particular. For our research, we reviewed the stress factors affecting flood disasters and performed surveys of two focus groups in Funchal municipality on Madeira Island (Portugal). Our research identifies the main causes of the serious impacts that flash floods can have on Funchal city. These include:

• The occupation of floodplain areas,
• Canalization of water courses,
• Erosion and transport of soil from upstream hillslopes due to deforestation, and
• Intensive precipitation of long duration.

     

Assessment of Presence of Heavy Metals and Other Pollution Burden Parameters and Their Effect on Water Quality in Benin City,Edo State

An assessment of the heavy metal content and biological pollution burden of an abattoir dumpsite, an automobile carwash, the Ikpoba River, and a private water borehole located near the abattoir dumpsite was conducted in Benin City in Southern Nigeria, during the rainy and dry seasons. The water samples’ pH was lower than the permissible values, with the river water displaying color and possessing a slight odor. Iron concentrations exceeded the permissible limits for drinking water at all four of the sampling stations in the study area, although not in samples collected from the control station, station 5, a public water borehole that is located some distance away. Lead values were higher than drinking water standards in the samples collected from stations 1 through 4, with some exceptions during the rainy season. Copper, nickel, arsenic, chromium, and aluminum were detected in three out of the four sampling stations located within the study area. However, these metals were detected at levels that were within regulatory limits. The detection of three metals (iron, lead, and zinc) within the study area at levels exceeding drinking water limits suggested that a complete cycle of movement of pollutants into the sampling stations had occurred. Significant biological presences were also detected, as the Ikpoba River water had a standard plate count (SPC) of 130 most probable number (MPN) and a 22 MPN presumptive coliform count (PCC) or 22 MPN. However, Escherichia coli (E. coli) confirmatory tests recorded less than 2 MPN in both seasons. The study has shown that the quality of water in the river may be compromised by effluent discharges from the dumpsite and the carwash channel. Therefore, this situation requires more stringent enforcement of local environmental laws and maintenance of safe distances between domestic and industrial waste sources and domestic or private water wells.     

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

SIMULATING THE IMPACT OF DRAINAGE DESIGN IN A COLD CLIMATE WITH ADAPT1

ABSTRACT: Surface and subsurface drainage make crop production economically viable in much of southern Minnesota because drainage allows timely field operations and protects field crops from extended periods of flooded soil conditions. However, subsurface drainage has been shown to increase nitrate/nitrogen losses to receiving waters. When engaging in drainage activities, farmers are increasingly being asked to consider, apart from the economic profit, the environmental impact of drainage. The Agricultural Drainage and Pesticide Transport model (ADAPT) was used in this study to evaluate the impact of subsurface drainage design on the soil water balance over a two‐year period during which observed drainage discharge data were available. Twelve modeling scenarios incorporated four drainage coefficients (DC), 0.64 cm/d, 0.95 cm/d, 1.27 cm/d, and 1.91 cm/d, and three drain depths, 0.84 m, 1.15 m, and 1.45 m. The baseline condition corresponded to the drainage system specifications at the field site: a drain depth and spacing of 1.45 m and 28 m, respectively (DC of 0.64 cm/d). The results of the two‐year simulation suggested that for a given drainage coefficient, soils with the shallower drains (but equal DC) generally have less subsurface drainage and can produce more runoff (but reduced total discharge) and evapotranspiration. The results also suggested that it may be possible to design for both water/nitrate/nitrogen reduction and crop water needs.     

NITROGEN AND ORGANIC MATTER LOSSES IN NO‐TILL CORN CROPPING SYSTEMS1

ABSTRACT: Intensive cropping systems based on mechanical movement of soil have induced land degradation in most agricultural areas due to soil erosion and soil fertility losses. Thus, farmers have been increasing fertilization rates to maintain an economically competitive crop yield. This practice has resulted in water quality degradation and lake eutrophication in many agricultural watersheds. Research was conducted in the Patzcuaro watershed in central Mexico to develop appropriate technology that prevents nonpoint source pollution from fertilizers. Organic matter (OM) and nitrogen (N) losses in runoff and nitrate (NO₃‐N) percolation in Andisols with corn under conventional till (CT) and no‐till (NT) treatments using variable percentages of crop residue as soil cover were investigated for steep‐slope agriculture. USLE type runoff plots were used to collect water runoff, while suction tubes with porous caps at 30, 60, and 90 cm depth were used to sample soil water solutes for NO₃‐N analyses. Results indicated a significant reduction of N and OM losses in runoff as residue cover increased in the NT treatments. Inorganic N in runoff was 25 kg/ha for NT without residue cover (NT‐0) and 6 kg/ha for the NT with 100 percent residue cover (NT‐100). Organic matter losses in runoff were 157 and 24 kg/ha for the NT‐0 and NT‐100 treatments, respectively. Nitrate‐N percolation was evident in CT and NT with 100 percent residue cover (NT‐100). However, NT‐100 had higher NO₃‐N concentration at the root zone, suggesting the possibility of reducing fertilization rates with the use of NT treatments.     

AN ECONOMIC ASSESSMENT OF GROUND WATER RECHARGE IN THE TUCSON BASIN1

ABSTRACT: Bringing water from Colorado River via the Central Arizona Project was perceived as the sole solution for Tucson Basin's water problem. Soon after Central Arizona Project's water arrived in Tucson in 1992, its quality provoked a quarrel over its use for potable purposes. A significant outcome of that quarrel was the enactment of the 1995 Proposition 200. The Proposition 200 precludes the use of Central Arizona Project's water for potable purposes, unless it is treated. Yet, it encourages using it for non‐potable purposes and for replenishing the Tucson aquifer through recharge. This paper examines the economic issues involved in utilizing Central Arizona Project's water for recharge. Four planning scenarios were designed to measure and compare the costs and benefits with and without Central Arizona Project's water recharge. Cost‐benefit analysis was utilized to measure recharge costs and benefits and to derive a rough estimate of cost savings from preventing land subsidence. The results indicate that the institutional requirements can be met with Central Arizona Project's water recharge. The economic benefits from reducing pumping cost and saving groundwater are not economically significant. Yet, when combining the use of Central Arizona Project's water for recharge and non‐potable purposes, it demonstrates positive net economic benefits.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.