Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response

The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5° × 5° latitude-longitude grids shows about 12-16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Copyright 2006 by the American Geophysical Union

Dynamics of a salinity-prone agricultural catchment driven by markets, farmers' attitude and climate change

An agent-based simulation model has been developed with CORMAS combining simplified bio-physical processes of land cover, dry-land salinity changes, rainfall, farm profitability and farmer decisions on land uses in a dry-land agricultural catchment (no irrigation). Simulated farmers formulate individual decisions dealing with land use changes based on the combined performance of their past land cover productivity and market returns. The willingness to adapt to market drivers and the ability to maximize returns varies across farmers. In addition, farmers in the model can demonstrate various attitudes towards salinity mitigation as a consequence of experiencing and perceiving salinity on their farm, in the neighborhood or in the entire region. Consequently, farmers can adopt land cover strategies aiming at reducing salinity impact. The simulation results using historical rainfall records reproduces similar trends of crop-pasture ratios, salinity change and farm decline as observed in the last 20 years in the Katanning catchment (Western Australia). Using the model as an explorative tool for future scenarios, the simulation results highlighted the importance of rainfall changes and wide-spread willingness of farmers to combat dry-land salinity. Rainfall changes as a consequence of climate change can lead to prolonged sequences of dry and wet seasons. Adaptation to these sequences by farmers seems to be critical for farm survival in this catchment. (Résumé d'auteur

A model of rainfall based on finite-state cellular automata

The purpose of this paper is to demonstrate that a finite state cellular automata model is suitable for modeling rainfall in the space-time plane. The time-series properties of the simulated series are matched with historical rainfall data gathered from Whenuapai, NZ. The spatial scale of the model cells in related to land-area by optimizing the cross-correlation between sites at lag 0 relative to rainfall data collected from Auckland, NZ. The model is shown to be adequate for simulation in time, but inadequate in spatial dimension for short distances

Bayesian approach to Spatio-temporally Consistent Simulation of Daily Monsoon Rainfall over India

Simulation of rainfall over a region for long time-sequences can be very useful for planning and policy-making, especially in India where the economy is heavily reliant on monsoon rainfall. However, such simulations should be able to preserve the known spatial and temporal characteristics of rainfall over India. General Circulation Models (GCMs) are unable to do so, and various rainfall generators designed by hydrologists using stochastic processes like Gaussian Processes are also difficult to apply over the vast and highly diverse landscape of India. In this paper, we explore a series of Bayesian models based on conditional distributions of latent variables that describe weather conditions at specific locations and over the whole country. During parameter estimation from observed data, we use spatio-temporal smoothing using Markov Random Field so that the parameters learnt are spatially and temporally coherent. Also, we use a nonparametric spatial clustering based on Chinese Restaurant Process to identify homogeneous regions, which are utilized by some of the proposed models to improve spatial correlations of the simulated rainfall. The models are able to simulate daily rainfall across India for years, and can also utilize contextual information for conditional simulation. We use two datasets of different spatial resolutions over India, and focus on the period 2000-2015. We propose a large number of metrics to study the spatio-temporal properties of the simulations by the models, and compare them with the observed data to evaluate the strengths and weaknesses of the models

A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity

This paper describes a rain-event driven, process-oriented simulation model, DNDC, for the evolution of nitrous oxide (N2O), carbon dioxide (CO2), and dinitrogen (N2) from agricultural soils. The model consists of three submodels: thermal-hydraulic, decomposition, and denitrification. Basic climate data drive the model to produce dynamic soil temperature and moisture profiles and shifts of aerobic-anaerobic conditions. Additional input data include soil texture and biochemical properties as well as agricultural practices. Between rainfall events the decomposition of organic matter and other oxidation reactions (including nitrification) dominate, and the levels of total organic carbon, soluble carbon, and nitrate change continuously. During rainfall events, denitrification dominates and produces N2O and N2. Daily emissions of N2O and N2 are computed during each rainfall event and cumulative emissions of the gases are determined by including nitrification N2O emissions as well. Sensitivity analyses reveal that rainfall patterns strongly influence N2O emissions from soils but that soluble carbon and nitrate can be limiting factors for N2O evolution during denitrification. During a year sensitivity simulation, variations in temperature, precipitation, organic C, clay content, and pH had significant effects on denitrification rates and N2O emissions. The responses of DNDC to changes of external parameters are consistent with field and experimental results reported in the literature

Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58% of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and three-hour precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions

Evaluation of the Water Quality Impacts of Land Application of Poultry Litter

Evaluating the effect of land application of animal waste on water quality is fraught with inherent variability due to differing infiltration rates, slope, rainfall intensity and etc . Simulated rainfall technology has been used in erosion research for decades. Generally, this technology is used on plots of sufficient size (25 x 5 m) to develop rill and interrill erosion. The object of this investigation was to adapt and modify existing rainfall simulation technology used in soil erosion research for use in evaluating water quality impacts of land application of animal waste, and to test, evaluate and demonstrate it\u27s scientific validity. State of the art simulation technology was obtained from the National Soil Erosion Research Laboratory located on the campus of Purdue University. The technology was scaled (2 x 6 m) and modified to fit into field research programs having several treatments and rep 1 i cated p 1 ots . The technology was shown to meet specification needed to produce the required raindrop size and velocity, flexibility in storm intensity, while maintaining uniformity(\u3e 0.8). Equally important, the unit is portable and fits well into labor intensive runoff work requiring replication of a variety of treatments