Role of the cloud adjustment time scale in simulation of the interannual variability of Indian summer monsoon

The simulation of precipitation in a general circulation model relying on relaxed mass flux cumulus parameterization scheme is sensitive to cloud adjustment time scale (CATS). In this study, the frequency of the dominant intra-seasonal mode and interannual variability of Indian summer monsoon rainfall (ISMR) simulated by an atmospheric general circulation model is shown to be sensitive to the CATS. It has been shown that a longer CATS of about 5 h simulates the spatial distribution of the ISMR better. El Nio Southern Oscillation-ISMR relationship is also sensitive to CATS. The equatorial Indian Ocean rainfall and ISMR coupling is sensitive to CATS. Our study suggests that a careful choice of CATS is necessary for adequate simulation of spatial pattern as well as interannual variation of Indian summer monsoon precipitation.

A Simulation Model for Unified Interrill Erosion and Rill Erosion on Hillslopes

A mathematical model was developed for simulating runoff generation and soil erosion on hillslopes. The model is comprised of three modules: one for overland flow, one for soil infiltration, and one for soil erosion including rill erosion and interrill er

The annual course of precipitation over much of the United States: observed versus GCM simulation

General Circulation Models (GCMs) may be useful in estimating the ecological impacts of global climatic change. We analyzed seasonal weather patterns over the conterminous United States and determined that regional patterns of rainfall seasonality appear to control the distributions of the Nation's major biomes. These regional patterns were compared to the output from three GCMs for validation. The models appear to simulate the appropriate seasonal climates in the northern tier of states. However, the spatial extent of these regions is distorted. None of the models accurately portrayed rainfall seasonalities in the southern tier of states, where biomes are primarily influenced by the Bermuda High.

Modeling of Hillslope Runoff and Soil Erosion at Rainfall Events Using Cellular Automata Approach

National Science Fund for Distinguished Young Scholars of China [40225004]; National Natural Science Foundation of China [40471048]

Simulation of monsoon disturbances in the LMD GCM

The monsoon depressions that form over India during the summer are analyzed using simulations from the Laboratoire de Meteorologie Dynamique general circulation model. This type of synoptic system often occurs with a frequency of one to two per month and can produce a strong Indian rainfall. Two kinds of analyses are conducted in this study. The first one is a subjective analysis based on the evolution of the precipitation rate and the pattern of the sea level pressure. The second one is an objective analysis performed using the TRACK program, which identifies and tracks the minima in the sea level pressure anomaly held and computes the statistics for the distribution of systems. The analysis of a 9-yr control run, which simulates strong precipitation rates over the foothills of the Himalayas and over southern India but weak rates over central India, shows that the number of disturbances is coo low and that they almost never occur during August, when break conditions prevail. The generated disturbances more often move north, toward the foothills of the Himalayas. Another analysis is performed to study the effect of the Tibetan Plateau elevation on these disturbances with a 9-yr run carried out with a Tibetan Plateau at 50% of its current height. It is shown that this later integration simulates more frequent monsoon disturbances, which move rather northwestward, in agreement with the current observations. The comparison between the two runs shows that the June-July-August rainfall difference is in large part due to changes in the occurrence of the monsoon disturbances.

Stochastic modelling of rainfall from satellite data

Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of • the difference in spatial support of the satellite information and independent data used for calibration • uncertainties on the independent calibration data • the non-Gaussian distribution of rainfall amount • the spatial intermittency of rainfall • the spatial correlation of the rainfall field This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Advances in the stochastic modelling of satellite-derived rainfall estimates using a sparse calibration dataset

As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

Hydropower Energy Simulation Using Mike 11 Model; A Case Study In South Germany's Small Run-Of-River Hydropower Plants

Renewable energy production is a basic supplement to stabilize rapidly increasing global energy demand and skyrocketing energy price as well as to balance the fluctuation of supply from non-renewable energy sources at electrical grid hubs. The European energy traders, government and private company energy providers and other stakeholders have been, since recently, a major beneficiary, customer and clients of Hydropower simulation solutions. The relationship between rainfall-runoff model outputs and energy productions of hydropower plants has not been clearly studied. In this research, association of rainfall, catchment characteristics, river network and runoff with energy production of a particular hydropower station is examined. The essence of this study is to justify the correspondence between runoff extracted from calibrated catchment and energy production of hydropower plant located at a catchment outlet; to employ a unique technique to convert runoff to energy based on statistical and graphical trend analysis of the two, and to provide environment for energy forecast. For rainfall-runoff model setup and calibration, MIKE 11 NAM model is applied, meanwhile MIKE 11 SO model is used to track, adopt and set a control strategy at hydropower location for runoff-energy correlation. The model is tested at two selected micro run-of-river hydropower plants located in South Germany. Two consecutive calibration is compromised to test the model; one for rainfall-runoff model and other for energy simulation. Calibration results and supporting verification plots of two case studies indicated that simulated discharge and energy production is comparable with the measured discharge and energy production respectively.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

Simulation of soybean growth and yield under northeastern Amazon climatic conditions

O objetivo deste trabalho foi parametrizar, calibrar e validar uma nova versão do modelo de crescimento e de produtividade da soja desenvolvido por Sinclair, em condições naturais de campo no nordeste da Amazônia. Os dados meteorológicos e os valores de crescimento e de área foliar da soja foram obtidos em um experimento agrometeorológico realizado em Paragominas, PA, de 2006 a 2009. As condições climáticas durante o experimento foram muito distintas, com uma ligeira redução na precipitação em 2007, em virtude do fenômeno El Niño. Houve redução no índice de área foliar (IAF) e na produção de biomassa neste ano, a qual foi reproduzida pelo modelo. A simulação do IAF apresentou raiz do erro quadrado médio (REQM) de 0,55 a 0,82 m2 m‑2, de 2006 a 2009. A simulação da produtividade da soja para os dados independentes apresentou um REQM de 198 kg ha‑1, ou seja, uma superestimativa de 3%. O modelo encontra-se calibrado e validado para as condições climáticas da Amazônia e pode contribuir positivamente para a melhoria das simulações dos impactos da mudança de uso da terra na região amazônica. A versão modificada do modelo de Sinclair simula adequadamente a formação de área foliar, a biomassa total e a produtividade da soja, nas condições climáticas do nordeste da Amazônia.

Insights of meso and micro-scale processes for the Caxiuanã forest region from high resolution simulation

Dados meteorológicos e simulações numéricas de alta resolução foram usados para estimar campos espaciais na região leste da Amazônia onde se situam a Floresta e a Baía de Caxiuanã, no Estado do Pará. O estudo foi feito para o período de Novembro de 2006, quando foi realizado o experimento de campo COBRA-PARÁ. Análises de imagens do sensor MODIS mostram a ocorrência de vários fenômenos locais como avenidas de nuvens, sistemas convectivos precipitantes, e importante influência das interfaces entre a floresta e as superfícies aquáticas. Simulações numéricas para o dia 7 de novembro de 2006 mostraram que o modelo representou bem as principais variáveis meteorológicas. Os resultados mostram que a Baía de Caxiuanã provoca importante impacto nos campos meteorológicos adjacentes, principalmente, através da advecção pelos ventos de nordeste que induzem a temperaturas do dossel mais frias a oeste da baía. Simulações de alta resolução (LES) produziram padrões espaciais de temperatura e umidade alinhados com os ventos durante o período diurno e mudanças noturnas causadas principalmente pela presença da baía e chuvas convectivas. Correlações espaciais entre os ventos de níveis médios e os fluxos verticais de calor latente mostraram que existe uma mudança de correlações negativas para as primeiras horas do dia passando para correlações positivas para o período da tarde e início da noite.

Evaluation of the Tropical Rainfall Measuring Mission 3B43 product over an inland area in Brazil and the effects of satellite boost on rainfall estimates

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

RELATIONS BETWEEN SOIL SURFACE ROUGHNESS, TORTUOSITY, TILLAGE TREATMENTS, RAINFALL INTENSITY AND SOIL AND WATER LOSSES FROM A RED YELLOW LATOSOL

The soil surface roughness increases water retention and infiltration, reduces the runoff volume and speed and influences soil losses by water erosion. Similarly to other parameters, soil roughness is affected by the tillage system and rainfall volume. Based on these assumptions, the main purpose of this study was to evaluate the effect of tillage treatments on soil surface roughness (RR) and tortuosity (T) and to investigate the relationship with soil and water losses in a series of simulated rainfall events. The field study was carried out at the experimental station of EMBRAPA Southeastern Cattle Research Center in Sao Carlos (Fazenda Canchim), in Sao Paulo State, Brazil. Experimental plots of 33 m(2) were treated with two tillage practices in three replications, consisting of: untilled (no-tillage) soil (NTS) and conventionally tilled (plowing plus double disking) soil (CTS). Three successive simulated rain tests were applied in 24 h intervals. The three tests consisted of a first rain of 30 mm/h, a second of 30 mm/h and a third rain of 70 mm/h. Immediately after tilling and each rain simulation test, the surface roughness was measured, using a laser profile meter. The tillage treatments induced significant changes in soil surface roughness and tortuosity, demonstrating the importance of the tillage system for the physical surface conditions, favoring water retention and infiltration in the soil. The increase in surface roughness by the tillage treatments was considerably greater than its reduction by rain action. The surface roughness and tortuosity had more influence on the soil volume lost by surface runoff than in the conventional treatment. Possibly, other variables influenced soil and water losses from the no-tillage treatments, e. g., soil type, declivity, slope length, among others not analyzed in this study.