JP2009008651A - Distributed run-off forecasting system using nation-wide synthetic radar rainfall - Google Patents
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Abstract
Description
本発明は、高精度で迅速な配信が可能なオンライン全国合成レーダ雨量及び同種のレーダ雨量と予測雨量を用い、地形、植生、土地利用、土壌、表層地質及び風化状態などで決まる流出特性等の水文学的要因をメッシュ毎に与えて流出計算を行う分布型流出モデルを組み合わせて現在から数時間先までの河道の任意地点における洪水流量を算出する事ができる流出予測システムに関する。 The present invention uses on-line nationwide synthetic radar rainfall that can be delivered quickly with high accuracy and the same type of radar rainfall and predicted rainfall, and such as runoff characteristics determined by topography, vegetation, land use, soil, surface geology, weathering conditions, etc. The present invention relates to a runoff prediction system capable of calculating flood discharge at any point on a river channel from the present to several hours ahead by combining a distributed runoff model that calculates runoff by giving hydrological factors for each mesh.
従来の流出予測システムとしては、例えば、流域をティーセン分割し地上雨量計の観測値で面積雨量を算出し、貯留関数法に代表される集中型流出モデルにより流域を数十ｋｍ２〜数百ｋｍ２に区分し、それら分割流域毎に斜面や降雨特性等を平均的、総体的にとらえる流出解析手法により流量および水位を予測している(特許文献１参照)。
しかしながら、上述した従来の流出予測システムで用いられている流域をティーセン分割し地上雨量計の観測値で面積雨量を算出する手法では、 流域内の面積雨量を正確に把握することは困難であり、降雨分布特性の違いによる洪水流出の違いを適切に表現できないといった問題がある。 However, it is difficult to accurately grasp the area rainfall in the basin with the method of dividing the basin used in the conventional runoff prediction system mentioned above and calculating the area rainfall with the observation value of the ground rain gauge, There is a problem that the difference in flood runoff due to the difference in rainfall distribution characteristics cannot be expressed properly.
また、従来の流出予測システムは、殆どの河川で貯留関数法に代表される集中型流出モデルが採用されているが、流域を数十ｋｍ２〜数百ｋｍ２に区分し、分割流域毎に斜面や降雨特性等を平均的、総体的にとらえる流出解析手法であるため、雨量分布や流域の地形、植生、土地利用、土壌、表層地質及び風化状態などで決まる浸透特性等の水文学的要因の場所的な違いを詳細に反映することができない。このため、そのつど解析に必要なパラメータを変更しなければ、流出特性の違いを正しく流出解析に反映することができず、さまざまな洪水流出波形に対して、一定のパラメータで、安定して高精度な解析を行うことは不可能であった。しかし、どのような洪水に対しても計算結果が整合するようパラメータをリアルタイムに変えることは困難であるため、洪水流出予測に用いるには精度に問題があった。 In addition, the conventional runoff prediction system employs a centralized runoff model represented by the storage function method in most rivers, but divides the basin into several tens of km 2 to several hundred km 2 for each divided basin. Hydrological factors such as infiltration characteristics determined by rainfall distribution, basin topography, vegetation, land use, soil, surface geology and weathering conditions, etc., because the runoff analysis method captures slopes and rainfall characteristics on an average and overall basis. It is not possible to reflect the difference in location in detail. For this reason, unless the parameters required for the analysis are changed each time, the difference in runoff characteristics cannot be correctly reflected in the runoff analysis. It was impossible to perform accurate analysis. However, since it is difficult to change the parameters in real time so that the calculation results match for any flood, there is a problem in accuracy when using it for flood runoff prediction.
従来、流出予測に用いる予測降雨は、外部から導入したり、経験等に基づく概略値を用いていたため、流域をメッシュに分割して、１０分毎に計算を行う分布型モデルによる洪水予測には、時間的、空間的、精度的に必ずしも十分な入力データではなかった。
分布型モデルの特徴としては、下記(ａ)乃至(ｄ)が挙げられる。
(a)レーダ雨量をメッシュ毎に与えることができるため、降雨の時空間分布を反映した流出量を求めることが可能。
（b）流域の流出特性を適切に反映したモデルとすることで、一定のパラメータを用いて、前期降雨の有無や、降雨特性の異なる種々の洪水流出を再現することが可能。
(ｃ)流域の任意の地点で流量を求めることが可能。
(ｄ)高水と低水を統合した物理的モデルを使用し、流域の物理的な特性を反映した定数を使用するため、流域特性が類似した他流域への適用も容易。
(e)土地利用を考慮した計算が可能なため総合治水計画などにも利用可能。
Conventionally, the forecast rainfall used for runoff prediction has been introduced from the outside, or approximate values based on experience, etc. have been used, so flood forecasting with a distributed model that divides the basin into meshes and calculates every 10 minutes However, the input data was not always sufficient in terms of time, space and accuracy.
The characteristics of the distribution model include the following (a) to (d).
(a) Since radar rainfall can be given for each mesh, it is possible to obtain runoff that reflects the temporal and spatial distribution of rainfall.
(B) By using a model that appropriately reflects the runoff characteristics of the basin, it is possible to reproduce the presence or absence of the previous rainfall and various flood runoffs with different rainfall characteristics using certain parameters.
(c) The flow rate can be obtained at any point in the basin.
(d) Uses a physical model that integrates high and low water and uses constants that reflect the physical characteristics of the basin, making it easy to apply to other basins with similar basin characteristics.
(e) Since it can be calculated in consideration of land use, it can also be used for comprehensive flood control plans.
従って、レーダ雨量と分布型モデルを用いた洪水予測は、雨量観測や洪水流量観測の実施されていない河川における洪水予測の可能性を広げるものである。 Therefore, flood prediction using radar rainfall and a distributed model expands the possibility of flood prediction in rivers where rainfall observation and flood flow observation are not implemented.
更に、当該流域に特化した降雨予測(降雨移動解析)を行なう事による利点として、下記の(ａ)、(ｂ)及び(ｃ)が挙げられる。
(ａ)当該流域に最適な範囲で適切な解析を行う事ができる。
(ｂ)レーダデータ入手後迅速(１〜２分)に降雨予測データ(１０分毎、１０分後〜１８０分後までの予測メッシュ雨量)を作成する事が可能、したがって、中小流域でも洪水予測を行なう事が可能。
(ｃ)１〜２分で１０分毎、１０分後〜１８０分後までの予測メッシュ雨量が算出できるため、他の予測雨量を使用する場合に比べ、非常に短時間間隔で流出予測を行なう事が可能
Furthermore, the following (a), (b), and (c) can be cited as advantages of performing rainfall prediction (rainfall movement analysis) specialized for the basin.
(a) Appropriate analysis can be performed within the optimum range for the basin.
(b) It is possible to create rainfall prediction data (predicted mesh rainfall from every 10 minutes, after 10 minutes to 180 minutes) quickly (1-2 minutes) after obtaining radar data. It is possible to do.
(c) Predictive mesh rainfall from 10 minutes to 180 minutes can be calculated every 10 minutes in 1 to 2 minutes, so runoff prediction is performed at a very short interval compared to using other predicted rainfalls. Possible
次に、分布型モデル開発の背景について説明する。我が国ではこれまで、貯留関数法やタンクモデル法などの集中型モデル（斜面集合型含む）により流出予測システムが構築されてきた。このようなモデルが採用された理由は、「(ａ)計算処理能力が低く、大量のデータ処理と保管が難しい。」、「(ｂ)地形、土地利用などの基礎データがアナログデータで提供されていたため、細部の詳細なデータの取得が難しい。」等であるが、近年これらの状況が改善されてきている。 Next, the background of distributed model development will be described. Up to now, in Japan, runoff prediction systems have been constructed using centralized models (including slope assembly type) such as the storage function method and tank model method. The reason for adopting such a model is that “(a) calculation processing capacity is low and it is difficult to process and store a large amount of data”, “(b) basic data such as topography and land use are provided as analog data. However, it is difficult to acquire detailed data in detail. ”However, these situations have been improved in recent years.
尚、現在では、詳細な数値計算を行う条件が整ってきており、現時点では、計算機の処理能力は１５年前と比較するとスピードで１００倍、記憶容量で１０００倍のオーダーで向上した。 At present, conditions for performing detailed numerical calculations are in place, and at present, the processing capacity of computers has improved by 100 times in speed and 1000 times in storage capacity compared to 15 years ago.
因に、地形データは、数値情報（国土地理院）、イコノス衛星データ（三菱商事）などにより詳細なデジタルデータが提供され始めている。また、土地利用も国土数値情報により詳細なデジタルデータが提供され始めている。更に、降雨データも国土交通省全国合成レーダ雨量等により詳細なデジタルデータが提供され始めている。同センターで高精度な予測雨量も提供されている等、詳細な数値計算を行う条件が整ってきている。 Incidentally, detailed digital data has begun to be provided for topographical data, such as numerical information (Geographical Survey Institute) and Ikonos satellite data (Mitsubishi Corporation). In addition, land use is starting to provide detailed digital data based on national land numerical information. Furthermore, detailed digital data has begun to be provided by the Ministry of Land, Infrastructure, Transport and Tourism nationwide synthetic radar rainfall. The conditions for detailed numerical calculations are in place, such as providing highly accurate forecast rainfall at the center.
更に、近年の地球環境の変化等による集中豪雨の増大、流域開発による土地利用の高度化により、洪水予測体制の整備が不十分な中小河川における洪水被害が増大している。これらの中小河川では、豪雨発生から被害発現までの時間も短く、かつ、洪水流量観測データの不足などにより、的確に災害状況を把握し非難勧告等を発令する事が困難であった。 Furthermore, flood damage in small and medium-sized rivers with insufficient flood forecasting systems is increasing due to the increase in torrential rain due to recent changes in the global environment and sophistication of land use through basin development. In these small and medium-sized rivers, the time from the occurrence of heavy rain to the onset of damage was short, and due to lack of flood flow observation data, it was difficult to accurately grasp the disaster situation and issue condemnation recommendations.
本発明は、このような従来の問題点に鑑みてなされたもので、降雨状況を面的に観測でき、高精度で迅速な配信が可能なオンライン全国合成レーダ雨量及び同種のレーダ雨量と予測雨量を用い、地形、植生、土地利用、土壌、表層地質及び風化状態などで決まる流出特性等の水文学的要因をメッシュ毎に与えて流出計算を行う分布型流出モデルを組み合わせて現在から数時間先までの河道の任意地点における洪水流量を算出する事ができる流出予測システムの提供を目的としたものである。 The present invention has been made in view of the above-described conventional problems, and is capable of observing rainfall conditions in a plane, and can be distributed quickly with high accuracy and on-line nationwide synthetic radar rainfall and similar types of radar rainfall and predicted rainfall. A few hours from the present by combining distributed runoff models that calculate runoff by giving hydrological factors such as runoff characteristics determined by topography, vegetation, land use, soil, surface geology and weathering conditions for each mesh The purpose is to provide a runoff prediction system that can calculate the flood discharge at any point along the river channel.
上述の如き従来の問題点を解決し、所期の目的を達成するため本発明の要旨とする構成は、オンライン全国合成レーダ雨量及び分布型流出モデルを用いた流出予測手段と、前記分布型流出モデルのモデル構造と、前記分布型流出モデルパラメータ設定手段と、前期流出予測に用いる全国合成レーダ雨量の検証手段及び補正手段と、前記流出予測システムの対象流域に特化した降雨移動解析を用いた降雨予測手段との全て又は何れかを選択又は組み合わせてなる分布型流出予測システムに存する。 In order to solve the conventional problems as described above and achieve the intended purpose, the present invention is composed of an outflow prediction means using an online national synthetic radar rainfall and a distributed runoff model, and the distributed runoff. The model structure of the model, the distributed runoff model parameter setting means, the national synthetic radar rainfall verification and correction means used for the previous runoff prediction, and the rainfall movement analysis specialized for the target basin of the runoff prediction system were used. It exists in the distributed runoff prediction system which selects or combines all or any one with a rain prediction means.
また、前記流出予測手段は、オンライン全国合成レーダ雨量及び同種レーダ雨量と予測雨量を用い、細分化しメッシュ毎に有効降雨モデル、地層流下モデル、及び河道流下モデルからなる分布型流出モデルにより、現在から数時間先までの河道の任意地点における流量を算出し予測画像を作成し又は表示するのが良い。 In addition, the runoff prediction means uses an online nationwide synthetic radar rainfall and the same type of radar rainfall and the forecasted rainfall, and subdivided into a mesh-type distributed runoff model consisting of an effective rainfall model, a stratum flow model, and a river flow model. It is preferable to calculate or display a prediction image by calculating a flow rate at an arbitrary point on the river channel up to several hours ahead.
更に、前記分布型流出モデルのモデル構造は、細分化しメッシュ毎に複数の層からなる地層流下モデル、有効降雨モデル、及び河道流下モデルからなり、流域の地形、植生、土地利用、土壌、表層地質などで決まる浸透特性等の水文学的要因を適切に反映して洪水の流出波形を再現するのが良い。 Furthermore, the model structure of the distributed runoff model is composed of a subsurface flow model consisting of multiple layers for each mesh, an effective rainfall model, and a river channel flow model, and the basin's topography, vegetation, land use, soil, surface geology It is better to reproduce the flood runoff waveform by appropriately reflecting hydrological factors such as infiltration characteristics determined by
更に、前記分布型流出モデルパラメータ設定手段は、各層の流れ、浸透、貯留の大きさを表す等価粗度、飽和透水係数、不飽和透水係数、間隙率などのパラメータと、それぞれの層厚は、地形、植生、土地利用、土壌分類、表層地質、風化状態、現地調査結果及び地質柱状図等をもとに流域の物理的な流出機構を反映するよう決定し、再現計算により十分な検証を行って決定するのが良い。 Furthermore, the distributed runoff model parameter setting means includes parameters such as equivalent roughness representing the flow, infiltration and storage size of each layer, saturated hydraulic conductivity, unsaturated hydraulic conductivity, porosity, and the respective layer thicknesses. Based on the topography, vegetation, land use, soil classification, surface geology, weathering condition, field survey results, geological columnar figures, etc., it is decided to reflect the physical drainage mechanism of the basin, and sufficient verification is performed by reproduction calculation It is good to decide.
また、前記全国合成レーダ雨量の検証手段及び補正手段は、レーダ雨量画像の目視点検を行うと共に、地上雨量計と地上雨量計に対応するメッシュのレーダ雨量と相関係数及び総雨量比を算出し検討することで定量的に判断し、さらに、レーダビームの遮蔽によるレーダ雨量の観測誤差の補正や、クラッタ等の発生時には隣接するクラッタの無いメッシュで補正等を行なうのが良い。 The nationwide synthetic radar rainfall verification means and correction means perform visual inspection of the radar rainfall image and calculate the radar rainfall, correlation coefficient and total rainfall ratio of the mesh corresponding to the ground rain gauge and the ground rain gauge. It is preferable to make a quantitative determination by studying, and to correct the radar rain observation error due to the shielding of the radar beam, or to correct with a mesh without adjacent clutter when clutter occurs.
また、前記降雨予測手段は、オンライン全国合成レーダ雨量及び同種のレーダ雨量を用い、対象流域に特化した降雨移動解析より得られる予測雨量により流出予測を行なうのが良い。 In addition, it is preferable that the rain prediction means use the on-line nationwide combined radar rainfall and the same type of radar rainfall to perform runoff prediction based on the predicted rainfall obtained from the rainfall movement analysis specialized for the target basin.
更に、オンラインレーダ雨量計を用いた補正を時々刻々行うレーダ雨量計全国合成システムにより作成されるため、約１ｋｍメッシュ単位、５分間隔で配信される全国合成レーダ雨量データ（現況）は高い精度を有している。 Furthermore, since it is created by a radar rain gauge nationwide synthesis system that makes corrections using an online radar rain gauge from moment to moment, nationwide synthesized radar rainfall data (current status) distributed at intervals of about 1 km in 5 minute intervals is highly accurate. Have.
当該流域における降雨予測は、オンライン合成レーダ雨量を用いて行ない、その他のメッシュ予測雨量との組み合わせ又は調整を行なう。 Prediction of rainfall in the basin is performed using online synthetic radar rainfall, and is combined with or adjusted with other mesh prediction rainfall.
また、流出モデルでは、流域特性を考慮し、できるだけ実現象に近い流出機構を反映したモデル化を行っているため、洪水波形の再現精度が向上する。そのため、降雨の時空間分布などの相違に基づく様々な洪水波形に対して、一定のパラメータを用いてさまざまな洪水に対して高精度な解析が可能であり、定数をその都度変えることのできない洪水予測に適している。 In addition, in the runoff model, the watershed characteristics are taken into account, and modeling that reflects the runoff mechanism that is as close as possible to the actual phenomenon is performed, so the flood waveform reproduction accuracy is improved. For this reason, it is possible to analyze various flood waveforms based on differences in the temporal and spatial distribution of rainfall, etc. with high accuracy for various floods using certain parameters, and the constants cannot be changed each time. Suitable for prediction.
更に、流域は地域区画（約１ｋｍメッシュ）毎に複数の層の流れと河道とにモデル化し、雨量は有効降雨モデルを通して与え、表面と河道内の流れはｋｉｎｅｍａｔｉｃ ｗａｖｅ法、地層内の流れはｄａｒｃｙ則により表現する。それぞれに用いるパラメータ、層圧等はメッシュ毎に地形、土地利用、植生、土壌分類、表層地質、風化状態、勾配などを考慮し、検証を実施して決定する。 Furthermore, the river basin is modeled into multiple layers of flow and river channels for each regional division (about 1 km mesh), rainfall is given through an effective rainfall model, the surface and flow in the river channel is the kinematic wave method, and the flow in the formation is darcy. Expressed by rules. The parameters, layer pressure, etc. used for each mesh are determined through verification, considering the topography, land use, vegetation, soil classification, surface geology, weathering conditions, gradients, etc. for each mesh.
メッシュに分割された流域について、数値地図（１ｋｍメッシュ平均標高、ＫＳ-２７３：流域界位置、ＫＳ-２７２：流路位置）に基づき落水線図を作成し、さらに目視よる修正を加える。落水線図は、単位メッシュ（１ｋｍメッシュ）からの流下方向を縦横４方向、斜４方向の合計８方向とし、単位メッシュを連ねて流下方向が連続するよう合成して流域全体の落水線モデルを構築する。 For the watershed divided into meshes, a waterfall diagram is created based on a numerical map (1 km mesh average elevation, KS-273: basin boundary position, KS-272: flow path position), and further visual correction is made. The waterfall diagram is composed of a total of 8 flow directions from the unit mesh (1 km mesh), 4 vertical and horizontal directions, and 4 slant directions. The unit meshes are combined so that the flow direction is continuous. To construct.
また、落水線に加えて国土数値情報の流路位置データに基づき河道を設定する。国土数値情報の土地利用区分、土壌分類、表層地質区分、風化状態、植生等に応じて区分し、表面流や浸透流を表すモデル構造やパラメータとして、層厚、等価粗度係数、水平透水係数、鉛直浸透能、最小水分量、飽和水分量等を設定する。具体的な手順は、現地調査結果、文献、経験値等により一次設定し、実測値との検証により最終パラメータを確定する。 In addition to the falling water line, the river channel is set based on the channel position data of the national land numerical information. Classification according to land use classification, soil classification, surface geological classification, weathering condition, vegetation, etc. of the national land numerical information, layer thickness, equivalent roughness coefficient, horizontal hydraulic conductivity coefficient as model structure and parameters representing surface flow and seepage flow Set the vertical osmotic capacity, minimum water content, saturated water content, etc. The specific procedure is to set primarily by field survey results, literature, experience values, etc., and final parameters are confirmed by verification with actual measurement values.
本発明は上述のように構成され、オンライン全国合成レーダ雨量及び同種のレーダ雨量を用いることで、流出解析精度に大きく影響する雨量分布データ（強度と分布）の精度が向上し、従来の地上雨量計で問題となっていた降雨分布の観測誤差による流出解析の誤差を解消することができるといった効果を奏する。 The present invention is configured as described above, and the accuracy of rainfall distribution data (intensity and distribution), which greatly affects the runoff analysis accuracy, is improved by using online nationwide synthetic radar rainfall and the same type of radar rainfall. There is an effect that it is possible to eliminate the error of runoff analysis due to the observation error of rainfall distribution, which was a problem in the total.
また、分布型流出モデルでは、地形、土地利用、植生、土壌分類、表層地質、風化状態など、降雨流出に関わる物理的な条件をメッシュ毎に設定することで、流出機構を物理的に解析することができるため、洪水波形の再現精度が向上し、定数を変えることなく、多様な洪水波形に対して高精度な流出計算を行うことができるといった効果を奏する。 In the distributed runoff model, the runoff mechanism is physically analyzed by setting the physical conditions related to rainfall runoff, such as topography, land use, vegetation, soil classification, surface geology, and weathering conditions, for each mesh. Therefore, the flood waveform reproduction accuracy is improved, and it is possible to perform highly accurate runoff calculation on various flood waveforms without changing the constants.
更に、任意の地点で流量が求められるという分布型流出モデルの特性を活かし、複数降雨、複数地点の流量観測測結果をもとに、総合的な検証を行うことができるため、特定地点の流量観測精度に左右されない高精度なモデル検証が可能である。 In addition, by taking advantage of the characteristics of the distributed runoff model that the flow rate is required at any point, it is possible to conduct comprehensive verification based on the results of flow observation at multiple points and multiple points. Highly accurate model verification independent of observation accuracy is possible.
また、観測データの整備された本川の基準点で検証を行ったモデルを作成し、観測データの少ない支川の任意地点において、洪水流量を算出することが可能である。 In addition, it is possible to create a model that has been verified at the reference point of the main river where observation data is prepared, and to calculate the flood discharge at any point of the tributary with little observation data.
更に、高精度な国交省レーダ雨量計のリアルタイムなデータや同種のレーダ雨量を用いて、洪水予測システム内で当該流域に特化した降雨移動解析を行うことにより、その地域に適合した適正な予測雨量を算出し、遅滞なく洪水予測を実施することができるといった効果を奏する。 Furthermore, by using the real-time data of the highly accurate MLIT radar rain gauge and the same type of radar rainfall, it is possible to conduct appropriate rainfall predictions that are appropriate for the region by conducting rainfall movement analysis specialized for the basin within the flood prediction system. It has the effect of being able to calculate rainfall and perform flood forecasts without delay.
オンライン全国合成レーダ雨量及び分布型流出モデルを用いた流出予測手段と、前記分布型流出モデルのモデル構造と、前期流出予測に用いる全国合成レーダ雨量の検証手段及び補正手段と、前記洪水予測システムの対象流域に特化した降雨移動解析を用いた降雨予測手段とを備える。 Runoff prediction means using online national synthetic radar rainfall and distributed runoff model, model structure of the distributed runoff model, national synthetic radar rainfall verification means and correction means used for the previous runoff prediction, and flood forecasting system And a rain prediction means using rain movement analysis specialized for the target basin.
以下、本発明に係る分布型流出予測システムの実施の一例を図面を参照しながら説明する。図中Ａは、本発明に係る分布型流出予測システムであり、この分布型流出予測システムＡは、図１に示すように、オンライン全国合成レーダ雨量及び分布型流出モデルを用いた流出予測手段１と、前記分布型流出モデルのモデル構造２と、前期流出予測に用いる全国合成レーダ雨量の検証手段３及び補正手段４と、前記洪水予測システムの対象流域に特化した降雨移動解析を用いた降雨予測手段５とを備える。 Hereinafter, an example of the implementation of the distributed spill prediction system according to the present invention will be described with reference to the drawings. A in the figure is a distributed runoff prediction system according to the present invention. As shown in FIG. 1, this distributed runoff prediction system A has runoff prediction means 1 using an online nationwide synthetic radar rainfall and a distributed runoff model. And the model structure 2 of the distributed runoff model, the nationwide synthetic radar rainfall verification means 3 and correction means 4 used for the previous runoff prediction, and the rainfall using the rain movement analysis specialized for the target basin of the flood prediction system And a prediction means 5.
前記流出予測手段１は、オンライン全国合成レーダ雨量及び同時刻オンライン全国合成レーダ雨量を用い、流域を第３次地域区画（約１ｋｍメッシュ）に細分化しメッシュ毎に複数の層からなる地層流下モデル及び河道モデルからなり、地形、流域の植生、土地利用、土壌分類、表層地質、風化状態などで決まる浸透特性等の水文学的要因を個別に捉えられる分布型流出モデルにより、降雨の河道への流出量を計算し、河道の任意地点における流出を算出し、水位予測画像を作成し又は表示するものである。 The runoff prediction means 1 uses an online national synthetic radar rainfall and an online national synthetic radar rainfall at the same time, and subdivides the basin into a third regional section (about 1 km mesh), Runoff to the river channel by a distributed runoff model that consists of river channel models and can capture hydrological factors such as infiltration characteristics determined by topography, basin vegetation, land use, soil classification, surface geology, weathering conditions, etc. The amount is calculated, the outflow at an arbitrary point in the river channel is calculated, and a water level prediction image is created or displayed.
また、前記分布型流出モデルのモデル構造２は、メッシュ毎に降雨から蒸発散等により失われる量を差し引いた有効降雨の、地表面における貯留と流れ、表層における浸透と貯留、土壌中における浸透と貯留、風化基岩層における浸透と貯留、基岩層における浸透と貯留を解析し、洪水の流出波形を再現するものである。 Further, the model structure 2 of the distributed runoff model is that the effective rainfall obtained by subtracting the amount lost due to evapotranspiration, etc. for each mesh, storage and flow on the ground surface, infiltration and storage in the surface layer, infiltration in soil Analyzes storage, infiltration and storage in weathered bedrock layer, infiltration and storage in bedrock layer, and reproduces flood runoff waveform.
更に、前記全国合成レーダ雨量の検証手段３及び補正手段４は、レーダ雨量画像の目視点検を行うと共に、地上雨量と地上雨量計の直上メッシュのレーダ雨量値との相関係数及び総雨比量を算出し検討することで定量的に判断し、また、レーダビームの遮蔽によるレーダ雨量の観測誤差の補正や、クラッタ発生時には隣接するクラッタの無いメッシュで補正を行なうものである。 Further, the verification means 3 and the correction means 4 for the nationwide synthetic radar rain amount visually inspect the radar rain amount image, and the correlation coefficient between the ground rain amount and the radar rain value of the mesh just above the ground rain gauge and the total rain ratio amount. Quantitative determination is made by calculating and studying, and the radar rain observation error is corrected by shielding the radar beam, and when clutter occurs, correction is performed with a mesh without adjacent clutter.
前記降雨予測手段５は、オンライン全国合成レーダ雨量及び同種のレーダ雨量を用い、対象流域に特化した降雨移動解析より得られる予測雨量により流出予測を行なうものである。 The rain prediction means 5 uses the on-line nationwide synthetic radar rainfall and the same type of radar rainfall to perform runoff prediction based on the predicted rainfall obtained from the rainfall movement analysis specialized for the target basin.
以下、流出予測手段１について更に詳しく説明する。従来の洪水予測や河川管理の分野における流出解析では、地上雨量観測所の観測結果として得られる点雨量を、各地上雨量計の配置から支配されるティーセン分割面積内に一様に与える流域平均雨量で実施してきた。 Hereinafter, the outflow prediction means 1 will be described in more detail. In conventional runoff analysis in the field of flood forecasting and river management, the average rainfall of the basin that uniformly gives the point rainfall obtained as a result of observation at the surface rainfall station within the Thiessen divided area controlled by the location of each ground rain gauge Has been implemented in.
しかしながら、地上の点雨量が必ずしもティーセン分割領域内の雨量を代表しているとは限らないため、しばしば計算流量と実績流量との誤差を生じる要因となっている。 However, since the point rainfall on the ground does not necessarily represent the rainfall in the Thiessen divided area, it often causes an error between the calculated flow rate and the actual flow rate.
これに対し、レーダ雨量計は、雨量強度の空間分布を平面的・時間的に連続して捉えることができるという特徴を有しているため、洪水予測や河川管理の分野における流出解析(計画流量算定等)に反映させることが望まれている。 On the other hand, radar rain gauges have the feature that they can capture the spatial distribution of rainfall intensity continuously in a plane and in time, so runoff analysis in the field of flood prediction and river management (planned flow rate) It is hoped that this will be reflected in the calculation.
以上のことから、選定した対象洪水については、レーダ雨量計観測データを優先的に使用する。尚、レーダ雨量(合成レーダ雨量)の検証３は、下記の条件により実施し、精度検証の条件としては、下記の(ａ)乃至(ｃ)が挙げられる。 Based on the above, radar rain gauge observation data will be used preferentially for the selected target floods. The radar rainfall (synthetic radar rainfall) verification 3 is performed under the following conditions, and the accuracy verification conditions include the following (a) to (c).
(ａ)比較雨量：合成レーダ雨量と地上雨量(点雨量)
(ｂ)比較地点：
地上雨量観測所（点雨量比較用）
(ｃ)精度検証：レーダ雨量画像、相関係数、総雨量比
(a) Comparison rainfall: Synthetic radar rainfall and ground rainfall (point rainfall)
(b) Comparison point:
Ground rainfall observation station (for spot rainfall comparison)
(c) Accuracy verification: radar rainfall image, correlation coefficient, total rainfall ratio
次に、斯かるレーダ雨量検証３の精度検証について説明する。合成レーダ雨量の精度検証に用いる指標値は、相関係数及び総雨量比とし算定条件・算定式は以下に示すとおりとする。 Next, the accuracy verification of the radar rainfall verification 3 will be described. The index values used to verify the accuracy of the combined radar rainfall are the correlation coefficient and the total rainfall ratio, and the calculation conditions and formula are as shown below.
また、算定条件は、地上雨量計の時間雨量系列をｘとする。レーダ雨量計の時間雨量（前１時間雨量）系列をｙとする。ｘは離散値、ｙは連続値であることから、ｘ≧１、ｙ≧０．５であるデータを対象に各指標値を算出する。ただし、これらのデータ数が５個未満の場合、各指標値は算出しない。 Further, the calculation condition is that the hourly rainfall series of the ground rain gauge is x. Let y be the hourly rainfall (previous hour rainfall) series of the radar rain gauge. Since x is a discrete value and y is a continuous value, each index value is calculated for data with x ≧ 1 and y ≧ 0.5. However, when the number of these data is less than 5, each index value is not calculated.
更に、相関係数算定式は、地上観測所の時間雨量とその観測所直上の１ｋｍメッシュのレーダ雨量をもとに、下式により相関係数を算出する。 Furthermore, the correlation coefficient calculation formula calculates the correlation coefficient by the following formula based on the hourly rainfall at the ground station and the 1 km mesh radar rainfall immediately above the station.
ここに、ｘｉ：時刻ｉの地上雨量（ｍｍ）、ｙｉ：時刻ｉのレーダ雨量（ｍｍ）、ｘ：平均地上雨量（ｍｍ）、ｙ：平均レーダ雨量（ｍｍ）、Ｎ：データ数である。 Here, xi: ground rainfall (mm) at time i, yi: radar rainfall (mm) at time i, x: average ground rainfall (mm), y: average radar rainfall (mm), and N: number of data.
また、総雨量比算定式は、地上観測所の時間雨量とその観測所直上の１ｋｍメッシュのレーダ雨量をもとに、下式により総雨量比を算出する。 The total rainfall ratio calculation formula calculates the total rainfall ratio by the following formula based on the hourly rainfall at the ground station and the radar rainfall of 1 km mesh just above the station.
ここに、ｘｉ：時刻ｉの地上雨量（ｍｍ）、ｙｉ：時刻ｉのレーダ雨量（ｍｍ）、Ｎ：データ数である。 Here, xi: ground rainfall (mm) at time i, yi: radar rainfall (mm) at time i, and N: number of data.
尚、対象降雨の選定にあたっては、下記の条件により実施した（対象降雨の選定条件）。
(ａ)総雨量比：点雨量評価＝０．５〜１．５の範囲、流域平均評価＝０．８〜１．２の範囲
(ｂ)相関係数：流域平均評価＝０．８以上
(ｃ)遮蔽：段差なし（１０ｍｍ／日未満）
(ｄ)グランドクラッター：発生なし（５分ピッチの時系列変化及び、累加雨量で判断）
この結果、検討より分布型流出モデルの検証計算に最適は降雨を選定する。尚、(ｃ)及び(ｄ)については補正を行い利用する。
In selecting the target rainfall, the following conditions were used (selection conditions for target rainfall).
(a) Total rainfall ratio: point rainfall evaluation = range of 0.5 to 1.5, basin average evaluation = range of 0.8 to 1.2
(b) Correlation coefficient: Basin average evaluation = 0.8 or more
(c) Shielding: no step (less than 10mm / day)
(d) Grand clutter: No occurrence (determined by time series change of 5 minute pitch and accumulated rainfall)
As a result, the optimum rainfall is selected for the verification calculation of the distributed runoff model. Note that (c) and (d) are used after being corrected.
次に、レーダ雨量補正手段４について説明する。グランドクラッターやレーダサイト近傍の抜けについては、レーダ降雨を補完する等の処理が必要であり、下記のように補正処理を実施する。 Next, the radar rainfall correction means 4 will be described. For missing in the vicinity of the ground clutter or the radar site, a process such as complementing the radar rainfall is necessary, and the correction process is performed as follows.
また、図２に示すように、レーダサイト近傍の抜けによるメッシュについて補正を行うため、図３のように対象区域の周辺メッシュの平均値を用いた補正を実施することとする。 Further, as shown in FIG. 2, in order to correct the mesh due to the omission in the vicinity of the radar site, correction using the average value of the surrounding mesh in the target area is performed as shown in FIG.
また、図４に示すように、レーダサイト近傍の降雨低減によるメッシュについて補正を行うため、図５のようにレーダ雨量計の距離特性を把握した上でレーダサイトからの距離に応じて補正を実施することとする。 In addition, as shown in FIG. 4, in order to correct the mesh due to rainfall reduction in the vicinity of the radar site, the distance characteristics of the radar rain gauge are grasped as shown in FIG. 5, and correction is performed according to the distance from the radar site. I decided to.
次に、分布型流出の計算手法について説明する。分布型モデル構造２は、［表１］に示すような３モデル区分された構造であり、有効降雨モデル、地層流下モデル、河道流下モデルにより構成する。地層流下モデル（地表、Ａ０層、Ａ＋Ｂ層、Ｃ１層、Ｃ２層）は水分を貯留する部分と流下する部分とを考慮してモデル化する。また、河道は国土数値情報により河道として識別される場合のみ設定する。 Next, a distributed outflow calculation method will be described. The distribution model structure 2 is a structure divided into three models as shown in [Table 1], and is composed of an effective rainfall model, a stratum flow model, and a river flow model. The formation flow model (the ground surface, the A0 layer, the A + B layer, the C1 layer, and the C2 layer) is modeled in consideration of a portion that stores water and a portion that flows down. A river channel is set only when it is identified as a river channel by national land numerical information.
また、単位メッシュ（約１×１ｋｍの３次地域区画）毎に、これら３モデル構造を構成していて、下流メッシュと結合することで流域を構成する。有効降雨モデルは樹幹及び窪地などの水分貯留及び蒸発散をモデル化し土地利用及び植生に応じて区分する。降雨（上流メッシュからの流入も）は有効降雨モデルに流入させ一旦貯留して、Ａ０層へ流下あるいはＡ＋Ｂ層へと浸透させる。 In addition, these three model structures are configured for each unit mesh (about 1 × 1 km tertiary area division), and a basin is configured by combining with the downstream mesh. The effective rainfall model models water storage and evapotranspiration such as trunks and depressions and classifies them according to land use and vegetation. (Inflow from the upstream mesh also) rainfall and stored temporarily flow into the effectiveness rain model is infiltrated into a stream into A 0 layer or A + B layer.
更に、地表から地下Ａ０層、Ａ＋Ｂ層、Ｃ１層、Ｃ２層など単位メッシュの一体構造を地画セルと命名し、この地画セルでの流下を地画流下モデルとして、有効降雨モデルから地下Ａ０層へ流下させる一方、Ａ＋Ｂ層へ浸透させる。Ａ０層厚を越えると地表を流下させ、地表流はｋｉｎｅｍａｔｉｃ ｗａｖｅ法で計算し、地下流はｄａｒｃｙ則で一体的に計算する。Ａ層以下の地下層は単位メッシュ毎に構成し、Ａ＋Ｂ層へ浸透するとＡ＋Ｂ層を流下あるいはＣ１層へ浸透させて、地下流はｄａｒｃｙ則で計算する。 Furthermore, underground A 0 layer from the surface, A + B layer, C1-layer, the integral structure of the unit mesh such as C2-layer named Tiga cell, the stream of this Tiga cell as Tiga falling model, underground from effective rainfall model while to flow down to the A 0 layer is infiltrated into the A + B layer. Passed down the surface exceeds A 0 thickness, overland flow is calculated as kinematic wave method, underground streams is calculated integrally with darcy law. The underground layer below the A layer is constructed for each unit mesh, and when it penetrates into the A + B layer, the A + B layer is made to flow down or into the C1 layer, and the ground downstream is calculated by the darcy rule.
尚、地表とＡ０層は一体構造として、土地利用比率に応じて山地域、都市域、水域の３種類の地画サブセルに区分する。河道セルは、セルとは独立に存在しているとして、この河道セルでの流下を河道流下モデルとして、ｋｉｎｅｍａｔｉｃ ｗａｖｅ法で計算する。これら分布型モデル構造を［表１］［表２］に、分布型モデル構造概念図及び分布型モデル構成図を図６に示す。 It should be noted that, as an integral structure is earth's surface and the A 0 layer, is divided in accordance with the land use ratio mountain areas, urban areas, to three types of Tiga sub-cell of the body of water. Since the river channel cell exists independently of the cell, the flow down the river channel cell is calculated by the kinematic wave method using the river channel flow model. These distribution model structures are shown in [Table 1] and [Table 2], and a distribution model structure conceptual diagram and a distribution model configuration diagram are shown in FIG.
［表１］
分 布 型 モ デ ル 構 造
[Table 1]
Distribution type model structure
［表２］
[Table 2]
分布型流出計算は、国土数値情報の地盤高や土地利用のメッシュデータを使用し、河川流域を細かくメッシュ区分して、流域の任意の地点での流出量を計算する手法である。 Distributed runoff calculation is a technique that uses the ground height and land use mesh data of the national land numerical information to finely classify the river basin and calculate the runoff at any point in the basin.
分布型モデル開発の背景について説明する。我が国ではこれまで、貯留関数法やタンクモデル法などの集中型モデル（斜面集合型含む）により洪水予測システムが構築されてきた。このようなモデルが採用された理由は、「(ａ)計算処理能力が低く、大量のデータ処理と保管が難しい。」、「(ｂ)地形、土地利用などの基礎データがアナログデータで提供されていたため、細部の詳細なデータの取得が難しい。」等であるが、近年これらの状況が改善されてきている。 The background of distributed model development will be explained. Until now, flood forecasting systems have been constructed in Japan using a centralized model (including slope assembly type) such as the storage function method and tank model method. The reason for adopting such a model is that “(a) calculation processing capacity is low and it is difficult to process and store a large amount of data”, “(b) basic data such as topography and land use are provided as analog data. However, it is difficult to acquire detailed data in detail. ”However, these situations have been improved in recent years.
尚、現在では、詳細な数値計算を行う条件が整ってきており、現時点では、計算機の処理能力は１５年前と比較するとスピードで１００倍、記憶容量で１０００倍のオーダーで向上した。 At present, conditions for performing detailed numerical calculations are in place, and at present, the processing capacity of computers has improved by 100 times in speed and 1000 times in storage capacity compared to 15 years ago.
因に、地形データは、数値情報（国土地理院）、イコノス衛星データ（三菱商事）などにより詳細なデジタルデータが提供され始めている。また、土地利用も国土数値情報により詳細なデジタルデータが提供され始めている。更に、降雨データも国土交通省全国合成レーダ雨量等により詳細なデジタルデータが提供され始めている。同センターで高精度な予測雨量も提供されている等、詳細な数値計算を行う条件が整ってきている。 Incidentally, detailed digital data has begun to be provided for topographical data, such as numerical information (Geographical Survey Institute) and Ikonos satellite data (Mitsubishi Corporation). In addition, land use is starting to provide detailed digital data based on national land numerical information. Furthermore, detailed digital data has begun to be provided by the Ministry of Land, Infrastructure, Transport and Tourism nationwide synthetic radar rainfall. The conditions for detailed numerical calculations are in place, such as providing highly accurate forecast rainfall at the center.
流域は、国土数値情報のＫＳ−２７０あるいはＫＳ−２７１ファイルより対象水系域を選定して、ＫＳ−２７３ファイルに示された流域界を原則として採用する。この流域界を基本として、目視判断法の原理を取り入れて自動化が可能な代表点数判断法（９代表地点）を採用して、単位流域ごとに代表地点数が最大のメッシュをその流域メッシュと考えて、メッシュ流域界を設定する（９代表地点のメッシュ数が同数の場合には、北側に位置する流域にその単位メッシュを含める）。 For the basin, the target water system basin is selected from the KS-270 or KS-271 file of the national land numerical information, and the basin boundary shown in the KS-273 file is adopted in principle. Based on this basin boundary, we adopt a representative point judgment method (9 representative points) that can be automated by incorporating the principle of visual judgment method, and consider the mesh with the largest number of representative points for each basin as the basin mesh. Then, mesh basin boundaries are set (if the number of meshes at the nine representative points is the same, the unit mesh is included in the basin located on the north side).
また、メッシュには、四角形メッシュを採用し、国土数値情報で採用されている標準地域メッシュ(単位メッシュ：１ｋｍ)で設定する。尚、この単位メッシュは以下に示す地画セルと河道セル（単位メッシュにより河道が無い場合もある）により構成される。 In addition, a quadrilateral mesh is adopted as the mesh, and the standard area mesh (unit mesh: 1 km) adopted in the national land numerical information is set. This unit mesh is composed of the following ground cell and river channel cell (there may be no river channel depending on the unit mesh).
因に、セルの種類としては、(ａ)地画セル：地表、地下、(ｂ)河道セル：河道、下水道、排水路、用水路が挙げられ、地画セル・河道セルの特徴としては下に掲げる［表３］のとおりである。 The types of cells include: (a) ground cell: surface, underground, (b) river channel cell: river channel, sewer, drainage channel, irrigation channel. It is as shown in [Table 3].
［表３］
[Table 3]
地画セル平面形状は、原則として既に示した単位メッシュと同じであるが、単位メッシュは、厳密には縦方向（南北方向）と横方向（東西方向）では延長が相違するが、流出計算には単位メッシュ面積の平方根として求めたメッシュ単位長（ｄ）を採用する。 Although the ground cell plane shape is the same as the unit mesh already shown in principle, the unit mesh is strictly different in the vertical direction (north-south direction) and lateral direction (east-west direction), but it is not suitable for outflow calculation. Adopts the mesh unit length (d) obtained as the square root of the unit mesh area.
但し、地画セルの流下方向は、縦横４方向と斜４方向の合計８方向に流下させるため、斜方向への地画セル形状は以下に示すように長［地画セル］地画セルは、地表と地下により構成し［表４］に示す構造とする。 However, since the flow direction of the ground cell is made to flow in a total of 8 directions of 4 vertical and horizontal directions and 4 diagonal directions, the ground cell shape in the diagonal direction is long [ground cell] ground cell as shown below. The structure shown in [Table 4] is composed of the ground surface and underground.
［表４］
ここに：ｄはメッシュ単位長＝地画セル単位長
尚、地画セル斜面勾配はメッシュ流域界内で隣接する地画セルの最急勾配方向へ流下するとして、その平均標高差ｈを斜面長Ｌで除した勾配θとする。
[Table 4]
Here: d is mesh unit length = ground cell unit length. Note that the slope gradient of the ground cell is assumed to flow in the direction of the steepest slope of the adjacent ground cell in the mesh basin boundary, and the average elevation difference h is the slope length. Let the gradient θ be divided by L.
国土地理院国土数値情報の土地利用分類は、１２種類に分類されているが、分布型モデルでは同じような流出特性の土地利用分類を集約し、下記の５大分類に再分類する。 The land use classification of the Geographical Survey Institute's national land numerical information is classified into 12 types. In the distribution model, land use classifications with similar runoff characteristics are aggregated and reclassified into the following five major classifications.
また、土地利用分類としては、下記の分類に大別することができる。
・分類１（山地）：森林、荒地
・分類２（畑地）：畑、果樹園、その他の樹木畑
・分類３（都市）：建物用地、幹線交通用地、その他の用地
・分類４（水田）：田
・分類５（水域）：内水地、海浜、海水域
因に、土木研究所では、土地利用別の等価粗度係数を以下の［表５］に示すように公表しており、これを考慮し、地表の土地利用は５大分類に集約し等価粗度係数(初期値)を設定する。
The land use classification can be roughly divided into the following classifications.
・ Category 1 (mountains): Forest, wasteland ・ Category 2 (fields): fields, orchards, and other tree fields ・ Category 3 (city): building land, main transportation land, other land ・ Category 4 (paddy field): Rice field ・ Category 5 (Water area): Inland waters, beaches, sea areas The Civil Engineering Research Institute has published the equivalent roughness coefficient by land use as shown in [Table 5] below. Considering this, land use on the surface of the earth is aggregated into five major categories and the equivalent roughness coefficient (initial value) is set.
［表５］
土地利用大分類別の等価祖度係数
[Table 5]
Equivalent profoundness coefficient by land use classification
地表の土地利用は５大分類に集約するが、流出現象は土地利用により異なり山地域や畑地域であれば地下Ａ０層も地表面と同様に大きな働きをするが、都市域では地下への浸透がほとんど無くＡ０層も形成されていないことから、流出計算には土地利用に応じた地表と地下Ａ０層までを含めたモデル化が必要である。 Land use on the surface is aggregated into five major categories, but the runoff phenomenon varies depending on land use, and if it is a mountain area or a field area, the underground A0 layer works as well as the surface of the ground, but in urban areas, Since there is almost no infiltration and no A0 layer has been formed, it is necessary to model the runoff calculation including the ground surface and underground A0 layer according to land use.
このため、土地利用の５大分類を踏まえて、地下Ａ０層の有無、地表から地下層への浸透の有無、により地画セル内を分類すると以下の［表６］に示す３タイプの地画サブセルに区分する。 Therefore, in light of the five major classifications of land use, whether underground A 0 layer, land of 3 types shown in the following Table 6 and the presence or absence of penetration from surface to the underground layer, by classifying the Tiga cell Divide into picture subcells.
［表６］
地画サブセルタイプ
また、地画サブセル３タイプの形状と地下Ａ０層とＡ＋Ｂ層の取り扱いと、土地利用大分類の関係は以下の［表７］に示すように考える。
[Table 6]
Ground subcell type
Further, the handling of Tiga subcell 3 types of shape and underground A 0 layer and the A + B layer, the relationship of land use large classification considered as shown in the following Table 7.
［表７］
地画サブセルタイプ別の形状
[Table 7]
Geography by subcell type
また、国土地理院国土数値情報の土地利用分類は、１２種類に分類されているが、同じような流出特性の土地利用分類を集約し、５大分類に再分類する。地画セルはこの５大分類土地利用を、地下Ａ０層の有無、地表から地下層への浸透の有無、により以下の［表８］に示す３タイプ地画サブセルに区分し各地画サブセルのタイプに応じた理論を使用する。 Moreover, the land use classification of the Geographical Survey Institute's national land numerical information is classified into 12 types, but the land use classifications with similar outflow characteristics are aggregated and reclassified into five major classifications. The Tiga cells utilize this 5 major classification land, whether underground A 0 layer, the penetration from the surface to the subterranean existence, the following sections and around image subcell 3 types Tiga subcell shown in Table 8 by Use theory according to type.
［表８］
地画サブセルタイプと土地利用分類
[Table 8]
Sub-cell type and land use classification
尚、河道セルは、たとえ河道幅が単位メッシュ（地画セルも同様）の一辺長を超えても、同一のセル内に存在すると仮定する。 It is assumed that the river channel cell exists in the same cell even if the river channel width exceeds one side length of the unit mesh (the same applies to the ground cell).
また、河道セル形状の設定根拠には、測量成果を利用する方法、国土数値情報を利用する方法、理論式を利用する方法があるが、最も精度の高い測量成果を利用する方法で、河道セル形状を設定する（［表９参照］）。 There are two methods for setting the river channel shape: a method using survey results, a method using national land numerical information, and a method using theoretical formulas. Set the shape (see Table 9).
［表９］
河道セル形状の設定根拠と設定方法
但し、河道の法勾配を考慮する場合は、上記の河道幅を河床幅とした台形断面とする。
[Table 9]
Basis and setting method of river channel cell shape
However, when considering the legal gradient of the river channel, the trapezoidal section with the river channel width as the river bed width is used.
また、分布型モデルでは最上流の単位メッシュから河道を配置することは、モデル構造上、可能であるが、一般的には、ある程度の単位メッシュを経て河道と見なせる排水路や小河川が形成されることから、集水面積を閾値（実際には集水面積に相当する単位メッシュ個数を閾値とする）として、閾値を越えた単位メッシュから河道が形成されているとして河道セルを配置する（河道を配置する基準は、集水面積に相当する単位メッシュ個数を閾値とする）。 In the distributed model, it is possible to arrange river channels from the uppermost unit mesh because of the model structure, but in general, drainage channels and small rivers that can be regarded as river channels are formed through some unit mesh. Therefore, using the catchment area as a threshold value (actually, the number of unit meshes corresponding to the catchment area is set as the threshold value), a river channel cell is arranged assuming that a river channel is formed from the unit mesh exceeding the threshold value (the river channel The standard for arranging the threshold is the number of unit meshes corresponding to the water collection area).
尚、河道粗度係数(初期値)の設定根拠には、洪水後の粗度係数検証結果、河相に応じた一般的値、河道計画祖度を利用する方法等から最適な方法を選択する。 As the basis for setting the river channel roughness coefficient (initial value), the optimum method is selected from the results of the roughness coefficient verification after flooding, the general value according to the river facies, and the method using the river channel design identity.
また、洪水予測地点を含む水位観測所では、河道水位を算出する必要があることから、既往調査で検討されているＨ−Ｑ曲線により計算流量から予測水位を算出する。 In addition, since it is necessary to calculate the river water level at the water level observatory including the flood prediction point, the predicted water level is calculated from the calculated flow rate using the HQ curve studied in the previous survey.
次に、モデル計算基本理論について、図７乃至図８を参照しながら説明する。分布型モデルによる流出量は、地画セルと河道セルに区分して、キネマティックウエーブにより計算する。 Next, the basic theory of model calculation will be described with reference to FIGS. The amount of runoff by the distributed model is calculated by kinematic wave by dividing it into a ground cell and a river channel cell.
有効降雨タンク及び地層流下モデルは、図７に示すように構成され、下記の連続式、運動方程式が成立するものである。
連続式
運動方程式
ここに、r:雨量(mm/hr)、h:水深(mm)、q1〜q3：流出高(mm/hr)、q4：浸透高(mm/hr)、QIN：前メッシュの流入高(mm/hr)、Mmin：最小水分量(mm)、Msat：飽和水分量(mm)、α1,α2：孔の係数(1/hr)、β：浸透能(mm/hr)、E：蒸発散量(mm/hr) 、
なお、β：浸透能はＡ＋Ｂ層により決定される。また上限値によりＡ＋Ｂ層への流量を制限できる機能を有する。
The effective rain tank and the geological flow model are configured as shown in FIG. 7, and the following continuous equation and equation of motion are established.
Continuous
Equation of motion
Where, r: rainfall (mm / hr), h: water depth (mm), q1 to q3: runoff height (mm / hr), q4: seepage height (mm / hr), QIN: inflow height of previous mesh (mm / hr), Mmin: Minimum water content (mm), Msat: Saturated water content (mm), α1, α2: Pore coefficient (1 / hr), β: Penetration capacity (mm / hr), E: Evapotranspiration (mm / hr),
Note that β: penetration ability is determined by the A + B layer. Moreover, it has a function which can restrict | limit the flow volume to an A + B layer with an upper limit.
表面及び表層は、図８(ｂ)に示すように構成され、それぞれ下記の連続式、運動方程式が成立するものである。
［表 面］
連続式 運動方程式
ここに、t：時間(hr)、x：位置(mm)、Q：単位幅表面流量(mm2/hr)、h：水深(mm)、θ：斜面勾配、n：等価粗度係数、re：有効降雨＋単位幅流入量(mm/hr)
The surface and the surface layer are configured as shown in FIG. 8B, and the following continuous equation and equation of motion are established respectively.
[Surface]
Continuous equation of motion
Where, t: time (hr), x: position (mm), Q: unit width surface flow rate (mm 2 / hr), h: depth of water (mm), θ: slope gradient, n: equivalent roughness coefficient, r e : Effective rainfall + Unit width inflow (mm / hr)
［表 層］
連続式
[Surface]
Continuous
運動方程式
ここに、r:雨量(mm/hr)、h：水深(mm)、QIN:流入量(mm/hr)、Q2：単位幅流出量(mm2/hr)、
QUP：越流量(mm/hr)、θ：斜面勾配、kX：水平方向の飽和透水係数(mm/hr)、
kｚ：鉛直方向の飽和透水係数(mm/hr)
Equation of motion
Here, r: rainfall (mm / hr), h: depth (mm), Q IN: inflow (mm / hr), Q 2 : unit width runoff (mm 2 / hr),
Q UP: Yue flow (mm / hr), θ: slope gradient, k X: saturated hydraulic conductivity in the horizontal direction (mm / hr),
k z : Saturated hydraulic conductivity in the vertical direction (mm / hr)
中間層上段及び中間層上段は、図８(ｃ)に示すように構成され、下記の連続式、運動方程式が成立するものである。
連続式
［運動方程式］
ここに、D:層圧(m)、QX:水平方向の流入量(m3/hr)、QZ:水平方向の流入量、
L:メッシュ長(m)、i:動水勾配、A:メッシュ底面積(m2)、b:定数、θ:水分量(h/D)、
kX及びkZ:水平及び鉛直方向の不飽和透水係数(m/sec)、θS:飽和水分量(SS2/D)
kSX及びkSZ:水平及び鉛直方向の飽和透水係数(cm/sec)、θW:最小水分量(SS1/D)QIN:流入量(mm/hr)、QUP：越流量(mm/hr)
なお、kZ kSZ鉛直方向の不飽和透水係数および鉛直方向の飽和透水係数は下段層により決定する。また上限値により下段層への流量を制限できる機能を有する。
The upper intermediate layer and the upper intermediate layer are configured as shown in FIG. 8C, and the following continuous equation and equation of motion are established.
Continuous
[Equation of motion]
Where D: bed pressure (m), Q X : horizontal inflow (m3 / hr), Q Z : horizontal inflow,
L: Mesh length (m), i: Hydrodynamic gradient, A: Mesh bottom area (m 2 ), b: Constant, θ: Water content (h / D),
k X and k Z : Unsaturated hydraulic conductivity in horizontal and vertical directions (m / sec), θ S : Saturated water content (S S2 / D)
k SX and k SZ : Horizontal and vertical saturated hydraulic conductivity (cm / sec), θ W : Minimum water content (S S1 / D) Q IN : Inflow rate (mm / hr), Q UP : Overflow rate (mm / hr)
Incidentally, the saturated hydraulic conductivity of k Z k SZ vertical unsaturated hydraulic conductivity and the vertical direction is determined by the lower layer. Moreover, it has the function which can restrict | limit the flow volume to a lower layer by an upper limit.
地下層は、図８(ｄ)に示すように構成され、下記の連続式、運動方式が成立するものである。
連続式
The underground layer is configured as shown in FIG. 8 (d), and the following continuous method and motion method are established.
Continuous
運動方程式
ここに、h：水深(mm)、QIN及びQ4': 流入量(mm/hr)、Q5：単位幅流出量(mm2/hr)、QUP：越流量(mm/hr)、θ：斜面勾配、kX：水平方向の不飽和透水係数(mm/hr)
Equation of motion
Where, h: depth of water (mm), Q IN and Q 4 ': inflow rate (mm / hr), Q 5 : unit width outflow rate (mm 2 / hr), Q UP : overflow rate (mm / hr), θ: slope gradient, k X : unsaturated hydraulic conductivity in the horizontal direction (mm / hr)
尚、河道の連続式、運動方程式は下記の通りである。
連続式
運動方程式
ここに、r:流入量(mm/hr)、t：時間(hr)、x：位置(mm)、h：水深(mm)、
Q：単位幅流量(mm2/hr)、θ：斜面勾配、n：等価粗度係数
The river channel continuity and equations of motion are as follows.
Continuous
Equation of motion
Where r: inflow rate (mm / hr), t: time (hr), x: position (mm), h: water depth (mm),
Q: Unit width flow rate (mm2 / hr), θ: slope gradient, n: equivalent roughness coefficient
以下、分布型流出モデルの作成手順について、図９を参照しながら説明する。分布型流出モデル作成手順としては、例えば、雄物川上流を対象にして、地形、土地利用、河道等の流域特性を反映できる洪水予測モデルとして、分布型流出モデルを作成する。 Hereinafter, a procedure for creating a distributed outflow model will be described with reference to FIG. As a procedure for creating a distributed runoff model, for example, a distributed runoff model is created as a flood prediction model that can reflect basin characteristics such as topography, land use, and river channels in the upper reaches of the Omono River.
図９は、分布型流出モデルの構築フローを示すものであり、本検討業務内容は、(ａ)流域のメッシュ分割、(ｂ)落水線図の作成、(ｃ)モデルの作成、(ｄ)検証洪水の水文データ整理、(ｅ)実績洪水検証の５項目である。
下表の［表１０］に、モデル作成時における利活用データ(数値地図)を整理し示す。
FIG. 9 shows the flow of construction of a distributed runoff model. The contents of this study are (a) mesh division of basin, (b) creation of waterfall diagram, (c) creation of model, (d) There are five items: hydrological data organization for verification floods and (e) actual flood verification.
[Table 10] below summarizes and shows utilization data (numerical map) at the time of model creation.
［表１０］
[Table 10]
更に、数値地図（１ｋｍメッシュ標高、ＫＳ−２７３：流域界位置、ＫＳ−２７２：流路位置）をもとに作成する。尚、作成にあたっては、単位メッシュ（１ｋｍ）からの流下方向を縦横４方向、斜４方向の合計８方向として、これら単位メッシュごとの流下方向を合成し流域全体の落水線モデル構造を作成する。 Further, it is created based on a numerical map (1 km mesh elevation, KS-273: basin boundary position, KS-272: flow path position). In the creation, the flow direction from the unit mesh (1 km) is set to a total of 8 directions of 4 vertical and horizontal directions and 4 oblique directions, and the flow direction for each unit mesh is synthesized to create a waterfall model structure of the entire basin.
また、分布型流出モデルの作成は、(ａ)有効降雨モデル、(ｂ)地層流下モデル、(ｃ)河道流下モデルに区分し行った。更に、有効降雨モデルの構造は、樹幹及び窪地貯留を考慮した有効降雨を地層モデルへ流下させる。有効降雨モデルのパラメータは、以下の［表１１］に示す項目を基に設定する。 In addition, the distribution runoff model was divided into (a) effective rainfall model, (b) geological flow model, and (c) river flow model. Furthermore, the structure of the effective rainfall model allows the effective rainfall considering the trunk and depression storage to flow down to the formation model. The parameters of the effective rainfall model are set based on the items shown in [Table 11] below.
更に、土地利用情報、・植生は、常緑広葉樹、落葉広葉樹、常緑針葉樹、落葉針葉樹、低木・草本である。その他、最小保水量部分は、蒸発散を考慮する。月毎の事前設定値またはリアルタイム観測値より算定する。 Furthermore, land use information and vegetation are evergreen broadleaf trees, deciduous broadleaf trees, evergreen coniferous trees, deciduous coniferous trees, shrubs and herbs. In addition, evapotranspiration is taken into consideration for the minimum water content. Calculate from monthly preset values or real-time observations.
［表１１］
[Table 11]
また、地層流下モデルの構造は、以下(ａ)乃至(ｅ)の５層構造とする。
(ａ)表面：表面流
表面流は、Ｋｉｎｅｍａｔｉｃ Ｗａｖｅ法にて計算する。表面のパラメータは、土地利用情報、・植生（常緑広葉樹、落葉広葉樹、常緑針葉樹、落葉針葉樹、低木・草本、その他）を基に設定する。
The structure of the geological flow model is the following five-layer structure (a) to (e).
(a) Surface: Surface flow The surface flow is calculated by the Kinematic Wave method. Surface parameters are set based on land use information and vegetation (evergreen broadleaf, deciduous broadleaf, evergreen conifer, deciduous conifer, shrub / herbaceous, etc.).
(ｂ)表層（Ａ０層）：表層内の流れ
表層内の流れは、Ｄａｒｃｙ則にて計算する。表層厚の設定は現地調査結果、柱状図及び森林水文学等を参考に層厚を設定する。表層のパラメータは、土地利用情報、・植生（常緑広葉樹、落葉広葉樹、常緑針葉樹、落葉針葉樹、低木・草本、その他）を基に設定する。
(b) Surface layer (A 0 layer): Flow in the surface layer The flow in the surface layer is calculated according to the Darcy law. The thickness of the surface layer is set by referring to the field survey results, column diagram, forest hydrology, etc. Surface parameters are set based on land use information and vegetation (evergreen broadleaf, deciduous broadleaf, evergreen conifer, deciduous conifer, shrub / herbaceous, etc.).
(ｃ)中間層上段（Ａ＋Ｂ層）：中間層早い流れ
中間層上段の流れは、水分量変化に伴う透水性変化を考慮した不飽和透水係数によりＤａｒｃｙ則にて計算する。中間層上段層厚の設定は現地調査結果及び柱状図等より分類毎に平均的な層厚を設定し、斜面勾配（１／４細分区画の最大傾斜角度及び最小傾斜角度の平均角度θ）を考慮した層厚（２ｃｏｓθ−１）をメッシュ毎に設定する。中間層上段のパラメータは、国土数値情報の土壌分類を「森林水文学」を参考に浸透性を大・中・小等に区分した項目を基に設定する。
(c) Middle layer upper stage (A + B layer): Middle layer fast flow The middle layer upper stage flow is calculated according to the Darcy law by the unsaturated water permeability coefficient considering the water permeability change accompanying moisture content change. The upper layer thickness of the intermediate layer is set according to the field survey results and columnar figures, etc., and the average layer thickness is set for each classification, and the slope gradient (average angle θ of the maximum and minimum inclination angles of the quarter subdivision) is set. The considered layer thickness (2 cos θ-1) is set for each mesh. The parameters in the upper half of the middle layer are set based on the soil classification of the national land information based on the items classified into large, medium and small with reference to “Forest Hydrology”.
(ｄ)中間層下段（Ｃ１層）：中間層の遅い流れ
中間層下段の流れは、水分量変化に伴う透水性変化を考慮した不飽和透水係数によりＤａｒｃｙ則にて計算する。中間層下段層厚の設定は、現地調査結果及び柱状図等より分類毎に平均的な層厚を設定し、斜面勾配（１／４細分区画の最大傾斜角度及び最小傾斜角度の平均角度θ）を考慮した層厚（２ｃｏｓθ−１）をメッシュ毎に設定する。中間層下段のパラメータは、国土数値情報の表層地質分類を現地調査結果等より浸透性（風化）を大・中・小等に区分した項目を基に設定する。
(d) Lower middle layer (C1 layer): Slow flow in the middle layer The flow in the lower middle layer is calculated according to the Darcy law using the unsaturated hydraulic conductivity taking account of the change in water permeability due to the change in water content. The middle layer lower layer thickness is set according to the field survey results and columnar figures, etc., and the average layer thickness is set for each classification, and the slope gradient (average angle θ of the maximum and minimum inclination angles of the quarter subdivision) The layer thickness (2 cos θ−1) taking into account is set for each mesh. The parameters in the lower half of the middle layer are set based on the classification of the surface geological classification of national land information based on the results of field surveys, etc., categorizing permeability (weathering) into large, medium and small.
(ｅ)地下層（Ｃ２層）：地下層の流れ
地下層の流れは、Ｄａｒｃｙ則にて計算する。地下層のパラメータは、国土数値情報の表層地質分類を「森林水文学」を参考に浸透性を大・中・小等に区分した項目を基に設定する。
(e) Underground layer (C2 layer): underground layer flow The underground layer flow is calculated according to the Darcy law. The parameters of the underground layer are set on the basis of the items classified into large, medium, small, etc. with reference to “forest hydrology” in the surface geological classification of the national land information.
また、河道流下モデルは、下表の［表１２］に河道流下モデルの作成に利用したデータ一覧表を示す。尚、河道流下モデルの作成にあたっては、国土地理院地形図の河道位置を精査し、国土数値情報を活用した。 The river flow model is shown in [Table 12] in the table below, which is a list of data used to create the river flow model. In creating the river channel flow model, the location of the river channel in the topographic map of the Geospatial Information Authority of Japan was scrutinized and the national land numerical information was used.
［表１２］
[Table 12]
一方、降雨予測手段５は、流域を対象とする洪水予測システムにあって、雨域移動解析プログラムの計算結果を入力として用いることができる。同プログラムでは、各種パラメータを最適化することにより地域や降雨特性に特化した計算を行うことが可能となっており、流域に特化した降雨予測範囲等のパラメータを実データによって検討・最適化した。 On the other hand, the rainfall prediction means 5 is a flood prediction system for a basin, and can use the calculation result of the rain area movement analysis program as an input. In this program, it is possible to perform calculation specialized for the region and rainfall characteristics by optimizing various parameters, and study and optimize parameters such as rainfall prediction range specialized for the basin with actual data. did.
また、当該流域の洪水予測モデル検討においては抽出された降雨より５洪水程度を選定する。対象洪水の選定に際しては、以下の点を判断の基準とした（検討対象降雨選定）。
(ａ)移動解析では最大５００ｋｍ四方程度の領域のデータを使用する。流域周辺のレーダ運用を踏まえ、パラメータ最適化はなるべく現在に近い条件を用いることが望ましい。
(ｂ)レーダ観測に欠測や異常があると移動解析計算に影響を及ぼすと考えられる。そのためデータは欠測や異常を含まない、あるいは無視できる程度であることが必要である。
(ｃ)目視によっても雨量の変動が激しく、移動の把握が困難なような降雨は移動解析によって高い精度を期待することはできない。このような降雨は優先度を下げ、移動特性のはっきりした降雨について的確な予測が行えるよう最適化を行うことの方が実践的である。
(ｄ)選定降水は降雨の多様性を持たせるため、降雨種別に多様性のある降雨を選定する。
In addition, in the flood prediction model study of the basin, about 5 floods are selected from the extracted rainfall. In selecting the target flood, the following points were used as criteria for judgment (selection of the target rainfall).
(a) In the movement analysis, data of a maximum area of about 500 km is used. Based on the radar operation around the basin, it is desirable to optimize the parameters as close to the present conditions as possible.
(b) If there are missing or abnormal radar observations, it is considered that the movement analysis calculation will be affected. Therefore, it is necessary that the data does not include missing data or anomalies or is negligible.
(c) It is not possible to expect high accuracy by rainfall analysis for rainfall where the amount of rainfall varies greatly and is difficult to grasp. It is more practical to reduce the priority of such rainfall and to optimize it so that it can accurately predict rainfall with a clear movement characteristic.
(d) In order to give the selected precipitation a variety of rainfall, select a rainfall with a variety of rainfall types.
更に、降雨予測計算は、雨域移動解析プログラムの可変パラメータより、特定地域の計算を行う上で最適化が必要と考えられるのは、下表の［表１３］に示す３個である。これらのパラメータについて、それぞれの選択可能範囲から適切な値の組合せを選定し、検討を行うものとした。先ず最適な計算範囲（移動ベクトル計算範囲）を決定し、次いでいくつかのタイムステップとメッシュサイズの組合せで移動解析計算を行った。 Furthermore, it is considered that the rain prediction calculation needs to be optimized to calculate a specific area from the variable parameters of the rain area movement analysis program, as shown in [Table 13] in the following table. For these parameters, appropriate combinations of values were selected from each selectable range and examined. First, the optimum calculation range (movement vector calculation range) was determined, and then the movement analysis calculation was performed with a combination of several time steps and mesh sizes.
［表１３］
検討対象パラメータ
ここでは、時間間隔、計算メッシュについては従来から使用されている値を用い、下記の３つの計算範囲設定で行われた移動解析の移動ベクトル分布及び計算結果のパターンを実況と比較することにより、流域の洪水予測システムに適当と思われる計算範囲を判定するものとした。ここでは荒川流域における計算範囲の例を示す。
[Table 13]
Parameters to consider
Here, by using the values used in the past for the time interval and calculation mesh, the movement vector distribution of the movement analysis performed in the following three calculation range settings and the pattern of the calculation result are compared with the actual situation, The calculation range considered to be appropriate for the flood forecasting system in the basin was determined. Here is an example of the calculation range in the Arakawa basin.
I．大領域ケース
中心３７度２５分、１４０度２０分 東西４００ｋｍ、南北４００ｋｍ
II．中領域ケース
中心３７度３５分、１４０度２０分 東西２００ｋｍ、南北２００ｋｍ
III．小領域ケース
中心３７度４３分、１４０度２０分 東西１００ｋｍ、南北１００ｋｍ
I. Large area case
Center 37 degrees 25 minutes, 140 degrees 20 minutes East-west 400km, north-south 400km
II. Middle area case Center 37 degrees 35 minutes, 140 degrees 20 minutes East-west 200km, north-south 200km
III. Small area case Center 37 degrees 43 minutes, 140 degrees 20 minutes East-west 100km, north-south 100km
また、通常の気象擾乱の移動速度は、速くても６０ｋｍ程度である。例えば、この移動速度で３時間先の雨量を計算する場合、１８０ｋｍ遠方の移動特性が影響し、大領域はそのような遠方も含む計算領域として設定したものである。 Moreover, the movement speed of a normal weather disturbance is about 60 km at the fastest. For example, when calculating rainfall for 3 hours ahead at this moving speed, the moving characteristics at a distance of 180 km are affected, and the large area is set as a calculation area including such a distant area.
一方、小領域は、対象流域内の移動特性が全体の場に左右されずに解析されることを重視したもの、中領域はその中間的なものという位置付けである。これらの範囲は、図１０に示すとおりである。雨域が南から移動してくることが多いために、当該流域から見た移動の上流側を重視し、範囲の中心を多少南に寄せている。 On the other hand, the small region is positioned so as to emphasize that the movement characteristics in the target basin are analyzed without being influenced by the entire field, and the middle region is positioned in the middle. These ranges are as shown in FIG. Since the rain area often moves from the south, the upstream side of the movement seen from the basin is emphasized, and the center of the range is moved slightly south.
計算された移動ベクトルの場と予測雨量分布は、図１１及び図１２に示すとおりである。この中で雨量分布の左端の図は、１時間毎の実況雨量分布、左から２枚目の図はその１時間前に行われた予測計算による１時間後予測雨量、３枚目の図は２時間前に行われた予測計算による２時間後予測雨量、４枚目の図は３時間前に行われた予測計算による３時間後予測雨量である。 The field of the calculated movement vector and the predicted rainfall distribution are as shown in FIGS. In this figure, the leftmost figure of the rainfall distribution is the actual rainfall distribution every hour, the second figure from the left is the predicted rainfall after one hour according to the prediction calculation performed one hour before, and the third figure is The predicted rainfall after 2 hours by the prediction calculation performed two hours ago The fourth figure shows the predicted rainfall after 3 hours by the prediction calculation performed three hours ago.
すなわち、各行の予測値はすべて同じ時刻を対象とする予測で、左端の図がその時刻の実況雨量である。実況の雨域移動は左端の図を順次下方に辿ったものである。一方、左端の図から斜め右下に辿ると左端を初期値とする１時間毎の予測を追うことになる。 That is, the prediction values for each row are all predictions for the same time, and the leftmost figure shows the actual rainfall at that time. The actual rain zone movement is the one in the left-most figure that has been traced downward. On the other hand, when tracing from the left end to the diagonally lower right, the hourly prediction with the left end as the initial value is followed.
尚、移動ベクトルが表示されていないのは、５０ｍ／ｓを上回る移動速度が検出されたため、適用がキャンセルされた場合である。その際には６時間前までの過去に遡って有効な移動ベクトル計算値があるかどうかを検索し、存在すればその値を用いて移動解析計算を行う。有効な計算値が無い場合、例えば最初の計算で移動が求まらなかった場合には移動なしとして計算されている。 The movement vector is not displayed when the application is canceled because a movement speed exceeding 50 m / s is detected. At that time, it is searched whether there is an effective movement vector calculation value retroactive to the past six hours ago, and if it exists, movement analysis calculation is performed using that value. When there is no effective calculation value, for example, when movement is not found in the first calculation, it is calculated as no movement.
また、大領域と中領域は、分布図の観察によっては差異が明確でなかったので、流域雨量を対象とする実況雨量と計算雨量の相関係数及び総雨量比を求め、比較を行った。相関係数、総雨量比は次の式によって算出した。実況値、予測値ともレーダ雨量を流域平均したものである。 In addition, since the difference between the large area and the middle area was not clear by observing the distribution map, the correlation coefficient and the total rainfall ratio between the actual rainfall and the calculated rainfall for the basin rainfall were compared and compared. The correlation coefficient and total rainfall ratio were calculated using the following formula. Both the actual value and the predicted value are basin averages of radar rainfall.
ここに、
ｘｉ：時刻ｉの流域平均雨量強度実況値（ｍｍ／ｈ）
ｙｉ：時刻ｉの流域平均雨量強度予測値（ｍｍ／ｈ）
ｘ：時間・流域平均雨量強度実況値（ｍｍ／ｈ）
ｙ：時間・流域平均雨量強度予測値（ｍｍ／ｈ）
Ｎ：データ数
である。
here,
xi: Actual value of basin average rainfall intensity at time i (mm / h)
yi: Estimated basin average rainfall intensity at time i (mm / h)
x: Time / basin average rainfall intensity actual value (mm / h)
y: Time / basin average rainfall intensity prediction (mm / h)
N: Number of data.
ここに、
ｘｉ：時刻ｉの流域平均雨量強度実況値（ｍｍ／ｈ）
ｙｉ：時刻ｉの流域平均雨量強度予測値（ｍｍ／ｈ）
Ｎ：データ数
である。
here,
xi: Actual value of basin average rainfall intensity at time i (mm / h)
yi: Estimated basin average rainfall intensity at time i (mm / h)
N: Number of data.
尚、本発明の分布型流出予測システムは、本実施例に限定されることなく、本発明の目的の範囲内で自由に設計変更し得るものであり、本発明はそれらの全てを包摂するものである。 The distributed runoff prediction system of the present invention is not limited to this embodiment, and can be freely modified within the scope of the object of the present invention, and the present invention encompasses all of them. It is.
１ 流出予測手段
２ モデル構造
３ 全国合成レーダ雨量の検証手段
４ 全国合成レーダ雨量の補正手段
５ 降雨予測手段
DESCRIPTION OF SYMBOLS 1 Runoff prediction means 2 Model structure 3 Nationwide synthetic radar rainfall verification means 4 Nationwide synthetic radar rain correction means 5 Rainfall prediction means
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