Current algorithms for computing contributing areas from a rectangular grid digital elevation model (DEM) use the flow-routing model of O'Callaghan and Mark (1984), which has two major restrictions: (1) flow which originates over a twodimensional pixel is treated as a point source (nondimensional) and is projected downslope by a line (one dimensional) (Moore and Grayson, 1991), and (2) the flow direction in each pixel is restricted to eight possibilities. We show that large errors in the computed contributing areas result for any terrain topography: divergent, convergent, or planar. We present a new model, called digital elevation model networks (DEMON), which avoids the above problems by representing flow in two dimensions and directed by aspect. DEMON allows computation of both contributing and dispersal areas. DEMON offers the main advantage of contour-based models (e.g., Moore et al., 1988), the representation of varying flow width over nonplanar topography, while having the convenience of using rectangular grid DEMs.

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A temperate deep lake, Lake Kuttara, Hokkaido 148 m depth at the deepest point was completely frozen in winter in the 20 th century. However, non-freezing of the lake over winter occurred four times in the 21 st century, which is probably due to global warming. In order to understand how thermal regime of the lake responds to climate change, its heat storage was calculated by estimating heat budget of the lake and monitoring water temperature at the deepest point for 1 June 2014-31 May 2016. As a result, the temporal variation of heat storage from the heat budget was very consistent with that from the direct temperature measurement the determination coefficient R 2 = 0.903. A sensitivity analysis was conducted by numerically changing main meteorological factors air temperature, solar radiation, wind speed, precipitation for the heat storage obtained from the heat budget estimate. The increase in air temperature and precipitation was very effective to increase the heat storage. It is noted that, considering the increasing rate of air temperature 0.024°C/yr , the lake could be permanently unfrozen in about two decades.

Abstract:In this article the relative roles of precipitation and soil moisture in influencing runoff variability in the Mekong River basin are addressed. The factors controlling runoff generation are analysed in a calibrated macro-scale hydrologic model, and it is demonstrated that, in addition to rainfall, simulated soil moisture plays a decisive role in establishing the timing and amount of generated runoff. Soil moisture is a variable with a long memory for antecedent hydrologic fluxes that is influenced by soil hydrologic parameters, topography, and land cover type. The influence of land cover on soil moisture implies significant hydrologic consequences for large-scale deforestation and expansion of agricultural land.

Large-scale climatic indices have been used as predictors of precipitation totals and extremes in many studies and are used operationally in weather forecasts to circumvent the difficulty in obtaining robust dynamical simulations of precipitation. The authors show that the sea level pressure North Pacific high (NPH) wintertime anomaly, a component of the Northern Oscillation index (NOI), provides a superior covariate of interannual precipitation variability in Northern California, including seasonal precipitation totals, drought, and extreme precipitation intensity, compared to traditional ENSO indices such as the Southern Oscillation index (SOI), the multivariate ENSO index (MEI), Niño-3.4, and others. Furthermore, the authors show that the NPH anomaly more closely reflects the influence of Pacific basin conditions over California in general, over groups of stations used to characterize statewide precipitation in the Sierra Nevada range, and over the southern San Francisco Bay region (NASA Ames Research Center). This paper uses the term prediction to refer to the estimation of precipitation (the predictand) from a climate covariate (the predictor), such as a climate index, or atmospheric moisture. In this sense, predictor and predictand are simultaneous in time. Statistical models employed show the effectiveness of the NPH winter anomaly as a predictor of total winter precipitation and daily precipitation extremes at the Moffett Field station. NPH projected by global climate models is also used in conjunction with atmospheric humidity [atmospheric specific humidity (HUS) at the 850-hPa level] to obtain projections of mean and extreme precipitation. The authors show that future development of accurate forecasts of NPH anomalies issued several months in advance is important for forecasting total winter precipitation and is expected to directly benefit water resource management in California. Therefore, the authors suggest that investigating the lead-time predictability of NPH anomalies is an important direction for future research.